UK, USA, Singapore

How digital transformation can be made good for all

Description:

The aim of this project investigated the associated risks in locating digital transformation strategies outside communities, as well as their preferences and priorities when moving into a digital environment. Specifically, the project theories that top-down approaches to digital transformation and digital readiness can overlook important demographic differences in communities in areas of digital literacy, digital familiarity, access to technology, and consent. 

The project initially looks at case studies in the UK and Singapore and the characteristics of vulnerable recipients in digital transformation, critiquing top-down approaches. Secondly, it examines current examples of ‘co-creation policies’ that can be found in the Chicago police community. This allows for the identification of understanding effective methods for citizen participation and obstacles to bottom-up policy development when disabled or disadvantaged communities are included. Lastly, the concept of Living Digital Transformation (LDT) is introduced and examined in terms of how bottom-up and user-centric digitising is a more sustainable and inclusive approach, in the context of university communities. 

Source: Findlay, M., & Shanmugam, S. (2023). Participatory Digital Futures: How digital transformation can be made good for all. SSRN, 2. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430120

UK, India, South Africa

Mindkind: Global mental health databank pilot

Description: 

Over the course of two years, Wellcome trust developed a pilot project which aimed at exploring the governance of data related to mental health. They joined with Sage Bionetworks to create a prototype and test the ability to build a global mental health databank (GMHD). This databank included longitudinal and electronically-derived data from youth, with an emphasis on the approaches, treatments, and interventions potentially relevant to anxiety or depression. To develop the project, a participatory data governance process was implemented involving youth aged between 14-24 in the decision-making process of the design, collection, sharing, analysis, reuse, and use of data by the researchers. The following participatory mechanisms were created: 

1. Young People’s Advisory Group: Each country had a Young People’s Advisory Group that met regularly online to discuss the study design and data collection. In total there were 32 participants from a target group of young people aged 16-24 with lived experience of mental health challenges.
2. Professional Youth Advisor: Each country recruited a full-time youth advisor as a member of the study team. Their role was to act as a link between the Young People’s Advisory Group and the project teams and support research directions. In total there were 3 professional youth advisors.
3. Global Youth Panel: The panel was composed by members with past experience on youth panels and advocacy groups for mental health issues. They met monthly to provide high-level feedback on project decisions. There were 15 participants in the panel.
4. International Youth Panel: Due to delays in the recruitment of young people for the Young People’s Advisory group, this panel was created ad hoc. The objective of this panel was to identify “active ingredients” of mental health, map data governance models and data collection strategies. 
5. Data Use Advisory Group: This group was responsible for providing scientific insights on the use of a global mental health databank and discussing its ethical issues. The members were from 7 countries (Australia, Brazil, India, Nigeria, South Africa, United Kingdom, and USA). There were 18 participants in the group.
6. Randomised Control Trial: A RCT was designed with participants from India, South Africa and the UK to test the governance conditions of the MindKind app after recording information about their mental health and behaviours. The sample was of 300 participants from a target group of young people that at least 10% had experienced mental health challenges.
7. Deliberative Democracy Sessions: Two rounds of deliberative democracy sessions were held in each country. There were in total 150 participants discussing different data governance models, benefits and concerns around data governance for a global mental health databank. 

Results: There was no statistically significant difference in enrollment rates across data governance models, even though the study expected that youngsters will prefer governance models that provide them more control over their data, they enrolled in the program regardless of the control over how their data was used or accessed. Moreover, participants given a choice of study topics showed a statistically significantly lower engagement with the study than participants who were assigned fixed study topics.

Source: 

Connected by Data. (n.d.). Connected by data | MindKind: Global mental health databank pilot. Connectedbydata.org. Retrieved May 11, 2023, from http://connectedbydata.org/cases/mindkind-global-mental-health-databank-pilot

UK, India, South Africa

Mindkind: Global mental health databank pilot

Description: 

Over the course of two years, Wellcome trust developed a pilot project which aimed at exploring the governance of data related to mental health. They joined with Sage Bionetworks to create a prototype and test the ability to build a global mental health databank (GMHD). This databank included longitudinal and electronically-derived data from youth, with an emphasis on the approaches, treatments, and interventions potentially relevant to anxiety or depression. To develop the project, a participatory data governance process was implemented involving youth aged between 14-24 in the decision-making process of the design, collection, sharing, analysis, reuse, and use of data by the researchers. The following participatory mechanisms were created: 

1. Young People’s Advisory Group: Each country had a Young People’s Advisory Group that met regularly online to discuss the study design and data collection. In total there were 32 participants from a target group of young people aged 16-24 with lived experience of mental health challenges.
2. Professional Youth Advisor: Each country recruited a full-time youth advisor as a member of the study team. Their role was to act as a link between the Young People’s Advisory Group and the project teams and support research directions. In total there were 3 professional youth advisors.
3. Global Youth Panel: The panel was composed by members with past experience on youth panels and advocacy groups for mental health issues. They met monthly to provide high-level feedback on project decisions. There were 15 participants in the panel.
4. International Youth Panel: Due to delays in the recruitment of young people for the Young People’s Advisory group, this panel was created ad hoc. The objective of this panel was to identify “active ingredients” of mental health, map data governance models and data collection strategies. 
5. Data Use Advisory Group: This group was responsible for providing scientific insights on the use of a global mental health databank and discussing its ethical issues. The members were from 7 countries (Australia, Brazil, India, Nigeria, South Africa, United Kingdom, and USA). There were 18 participants in the group.
6. Randomised Control Trial: A RCT was designed with participants from India, South Africa and the UK to test the governance conditions of the MindKind app after recording information about their mental health and behaviours. The sample was of 300 participants from a target group of young people that at least 10% had experienced mental health challenges.
7. Deliberative Democracy Sessions: Two rounds of deliberative democracy sessions were held in each country. There were in total 150 participants discussing different data governance models, benefits and concerns around data governance for a global mental health databank. 

Results: There was no statistically significant difference in enrollment rates across data governance models, even though the study expected that youngsters will prefer governance models that provide them more control over their data, they enrolled in the program regardless of the control over how their data was used or accessed. Moreover, participants given a choice of study topics showed a statistically significantly lower engagement with the study than participants who were assigned fixed study topics.

Source: 

Connected by Data. (n.d.). Connected by data | MindKind: Global mental health databank pilot. Connectedbydata.org. Retrieved May 11, 2023, from http://connectedbydata.org/cases/mindkind-global-mental-health-databank-pilot

UK

Belfast to launch “Citizen Office of Digital Innovation”

Description:

In 2022, Belfast, located in Northern Ireland launched and piloted a program called Citizen Office of Digital Innovation (CODI). It aimed to increase resident engagement when it comes to data and technology. Overall, the program looks to support ‘digital citizenship skills’ which are part of the Smart Belfast Programme. This looks to help citizens to develop a better understanding of how technology is utilized in Belfast. Within the program, creative and interactive methods of engagement were used to explore concepts like co-design, citizen science, the Internet of Things, Artificial Intelligence, data, science, and privacy.  

Source: Wray, S. (Ed.). (2022, September 1). Belfast to launch “Citizen Office of Digital Innovation.” Cities Today. https://www.itu.int/hub/2022/09/belfast-to-launch-citizen-office-of-digital-innovation/

UK

Care.data

Description: 

The program care.data was made public in 2013 by the Health and Social Care Information Centre. The goal was to use data collected by GP surgeries and store them in a central database using the General Practice Extraction Service (GPES). Those who were part of GP practices were instructed that their health information would be stored in the database. Members of the GP practices who did not want their information to be uploaded were allowed to object. 

In this database, patients’ data was anonymized and could be accessed by health care researchers, managers and planners within the NHS, as well as academic institutions and organizations. The software and services used for this program were provided by Atos. 

The care.data program was heavily criticized and was deemed controversial since it was launched due to many overlooked factors. It was first critiqued around the lack of patient awareness regarding the program as well as lacking obvious options for objecting in the leaflet sent to homes. Additionally, the leaflet only described the program and the benefits surrounding it but did not include the opt-out form. 

After multiple reviews, the care.data program ended due to “major issues with project definition, schedule, budget, quality and/or benefits delivery, which at this stage do not appear to be manageable or resolvable” (Ramesh, 2015). Moreover, in December 2015 Atos was criticized by the Public Accounts Committee and was accused of taking advantage of the Department of Health. After a few years, the program ended in July 2016. 

The authors of The social license for research: why care.data ran into trouble argue that despite obtaining a lawful infrastructure for the implementation of the program, no social license was secured which led to challenges. 

Care.data could have been successful if three areas were recognized:

1. Establishing trust and confidence in the governance of research needs to go beyond focusing on economic gains and also consider the patient’s concerns as an individual seeking care. Meaning, their concerns as a citizen, who is part of a larger social fabric, is different to the concerns as an individual patient. 
2. In order for initiatives like care.data to be successful, patients must have confidence that their medical records will be securely and appropriately managed, with consideration of anonymization and public interest. 
3. Respecting the conditions of the social license involves upholding principles such as reciprocity, avoiding exploitation and prioritizing the public good.  

Source: Carter, P., Laurie, G. T., & Dixon-Woods, M. (2015). The social licence for research: whycare.dataran into trouble. Journal of Medical Ethics, 41(5), 404–409. https://doi.org/10.1136/medethics-2014-102374

Additional Links: 

●      Ramesh, R. (2015, June 26). NHS patient data plans unachievable, review finds. The Guardian. https://www.theguardian.com/politics/2015/jun/26/nhs-patient-data-plans-unachievable-review-health

●      Wikipedia . (2020, July 31). Care.data. Wikipedia. https://en.wikipedia.org/wiki/Care.data

Germany

Citizen Engagement and Innovative Data Use for Africa’s Development (DataCipation)

Description: 

The programme supports AU Organs in their quest to intensify citizen engagement and the role of data, digital and non-digital approaches in their programmes and initiatives. The programme is implemented in cooperation with the African Union Commission and the AU Development Agency (AUDA-NEPAD). The political partner for the programme is the Bureau of the Chairperson at the African Union Commission, demonstrating the high political commitment of the AU to the programme.

The programme takes a systemic approach, focusing on implementation across three main areas as follows:

1. Connecting policymakers with Africa’s data and digital innovators for good governance and development by enhancing the collaboration and cooperation of the AU Organs and Member States with Africa’s digital innovation ecosystem.
2. Improving citizen participation in good governance and development through innovative communications and engagement methodologies; leveraging data and digital and non-digital approaches.
3. Supporting the implementation of digital policies across Africa to improve access to meaningful participation of citizens in the digital transformation and to exploit the related potentials for social and economic development

Success factors included, enabling the African Union Commission to lead by example for digital transformation in public sector innovation as well as interactive, participatory communications efforts.Secondly it allowed for the building coalitions of the willing of AU Member States for spearheading novel digital policy approaches that pave the way for broader adoption at continental level. And lastly it permitted for a trusted partner to AUC and African policymakers by providing independent expertise geared towards realising the strategic interests including techno-geopolitical sovereignty of the African continent. 

Source: giz. (2021, December). Citizen Engagement and Innovative Data Use for Africa’s Development (DataCipation). Www.giz.de. https://www.giz.de/en/worldwide/98533.html

UK

Control Access to Patient Records for Research in the UK

Description: 

The secondary use of health data for research raises complex questions of privacy and governance. Such questions are ill-suited to opinion polling where citizens must choose quickly between multiple-choice answers based on little information. Two citizens´ juries, of 17 citizens each, were convened for three days to reflect on what control informed citizens would seek over the use of health records for research. Each jury answered, “To what extent should patients control access to patient records for secondary use?”. Jurors heard from and questioned five expert witnesses. Their individual views were polled using questionnaires at the beginning and at the end of the process.

Results: 

33 out of 34 jurors voted in support of the secondary use of data for research, with 24 wanting individuals to be able to opt out, six favoring opt-in, and three voting that all records should be available without any consent process. When considering who should get access to data, both juries had very similar rationales. Both thought that public benefit was a key justification for access. Jury 1 was more strongly supportive of sharing patient records for public benefit, in contrast, jury 2 was more cautious and sought to give patients more control.

The findings show that, when informed of both risks and opportunities associated with data sharing, citizens believe an individual’s right to privacy should not prevent research that can benefit the general public. 

Source: Tully, M. P., Bozentko, K., Clement, S., Hunn, A., Hassan, L., Norris, R., Oswald, M., & Peek, N. (2018). Investigating the Extent to Which Patients Should Control Access to Patient Records for Research: A Deliberative Process Using Citizens’ Juries (2nd ed., Vol. 20). Journal of Medical Internet Research. https://www.jmir.org/2018/3/e112

Spain

Digital Rights Governance Framework

Description: 

Digital technologies influence change at a fast pace in society, and might have harmful impact on individuals and communities. Against this backdrop, cities need enhanced models of governance to manage opportunities and risks driven by technology and ensure digital rights, which ultimately are human rights in the digital space, are protected and promoted. The proposal for the Digital Rights Governance Framework is a normative, yet pragmatic framework for the city-wide implementation of digital rights that unfolds the foundations, structures and tools necessary for developing a rights-based governance of the digitalisation of municipal services. It is established through (1) determining a city’s core values, (2) translating these core values into thematic areas (e.g transparency, autonomy, equity and participation), (3) combing both core values and thematic areas and choose a digital human rights understanding in the format of a bill of digital rights, a data-policy with sovereignty or a code of ethics.

Methods for public engagement and community participation:

●      Establishment of civil society groups and representatives who are willing to engage in consultations and actively participate in initiatives 

●      (Short-term projects) Arrangement of public consultations and focus groups to involve city residents which involve designing and implementing new strategies. 

●      (Long term projects) Arrangement for representatives and civil society organisations to be involved in policy developments, to ensure marginalised voices are heard. 

●      Establishment of a system for civil society organisation to promptly inform municipalities for urgent manners. 

●      Development of a comprehensive inventory of potential digital rights “violations” in the city, engage in public dialogues, and seek to put on strategies for addressing them. 

●      Community Manager to facilitate the contact with these stakeholders.

●      Organisation of public consultations on new and impactful digital policies.

Source: Cities Coalition for Digital Rights, & Un Habitat. (n.d.). DIGITAL RIGHTS GOVERNANCE FRAMEWORK. https://citiesfordigitalrights.org/sites/default/files/DIGITAL%20RIGHTS%20FRAMEWORK_CONCEPT%20FOR%20FEEDBACK.pdf

UK

Explainable AI in the UK

Description: 

Two juries were selected, one in Coventry and one in Manchester, with the purpose of analysing the importance the public gives to receiving an explanation when AI was used in their healthcare, focusing on the tradeoff between the accuracy and explainability of AI systems. The jurors were made up of a cross-section of the population, representing the demographic breakdown of England as per the 2011 Census. In total, 36 individuals were selected to be jurors. They considered the tradeoff accuracy-explainability in four different scenarios: 

1. Healthcare: diagnosis of acute stroke 
2. Healthcare: finding matches between kidney transplant donors and recipients 
3. Criminal Justice: deciding which offenders should be referred to a rehabilitation programme 
4. Recruitment: screening job applications and making shortlisting decisions

Results: 

Three key themes relating to explaining AI decisions emerged from the research:

1. The importance of context for the relevance of the explanation required
2. The need for education and awareness on the use of AI for decision-making
3. The challenges in providing explanations of AI at the expense of less accurate decision-making

Most jurors felt that the relative importance of explanations and accuracy varied by context. In contexts where humans would usually provide an explanation, most jurors indicated that explanations of AI decisions should be similar to human explanations. Jurors felt this was important to help build trust and to ensure explanations were understandable.

Source: Information Commissioner’s Office. (2019). Project ExplAIn Interim Report. https://ico.org.uk/media/2615039/project-explain-20190603.pdf

UK

Fair uses of NHS patients´ data and NHS operational data

Description:

A public engagement program was held to understand what people think about the NHS allowing third parties to access their health data, ranging from academics, charities, or industries. The analysis centred on the system of rules and environment that would make a health system trustworthy. 

 The mixed methods used were:

1. Three round tables involving patient representatives in Oxford, Manchester, and London: their aim was to design the stimulus materials and the method of testing the charge question for the Citizen’s Jury. There were a total of 30 patient representatives. 
2. Citizen’s Juries in Taunton, Leeds, and London:  to discuss the question, ‘What constitutes a fair partnership between the NHS and researchers, charities and industry on uses of NHS patients’ data and NHS operational data?’. This involved 60 jurors over two and a half days.
3. A nationally representative survey from the UK: 2095 people completed the survey. It quantitatively explored important topics jurors focused on and tested broader public opinion on several key themes that emerged including the level of awareness of data access partnerships in a representative sample and aspects of communication raised by jurors. 

Results:

Specifically looking at the results around citizens’ involvement the jurors decided that citizens need to be more involved at different decision-making stages, especially in policy and practices. Meaning, citizens should be present and encouraged to participate in the process of establishing and managing data access partnerships. The jurors identified three engagement management processes that can be used to include citizens: 

1. Citizens´ Juries and deliberation for key decisions
2. Public votes to approve local partnerships
3. Playing a role in governance boards

Moreover, jurors proposed that each data access partnership should publish reports and case studies to provide transparency to the public. Moreover, jurors concluded that communities and individuals would be more resistant to data access partnerships using their data if procedures and methodologies are unclear. To increase trust, it is essential to create an awareness campaign about the national data opt-out service to increase trust and confidence in the system. 

Source: Hopkins, H., Kinsella, S., Van, A., Hopkins, M., & Mil, V. (2020). Foundations of fairness: views on uses of NHS patients’ data and NHS operational data A mixed methods public engagement programme with integrated Citizens’ Juries Findings Report. https://understandingpatientdata.org.uk/sites/default/files/2020-03/Foundations%20of%20Fairness%20-%20Full%20Research%20Report.pdf

UK, USA, Singapore

How digital transformation can be made good for all

Description:

The aim of this project investigated the associated risks in locating digital transformation strategies outside communities, as well as their preferences and priorities when moving into a digital environment. Specifically, the project theories that top-down approaches to digital transformation and digital readiness can overlook important demographic differences in communities in areas of digital literacy, digital familiarity, access to technology, and consent. 

The project initially looks at case studies in the UK and Singapore and the characteristics of vulnerable recipients in digital transformation, critiquing top-down approaches. Secondly, it examines current examples of ‘co-creation policies’ that can be found in the Chicago police community. This allows for the identification of understanding effective methods for citizen participation and obstacles to bottom-up policy development when disabled or disadvantaged communities are included. Lastly, the concept of Living Digital Transformation (LDT) is introduced and examined in terms of how bottom-up and user-centric digitising is a more sustainable and inclusive approach, in the context of university communities. 

Source: Findlay, M., & Shanmugam, S. (2023). Participatory Digital Futures: How digital transformation can be made good for all. SSRN, 2. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430120

UK, India, South Africa

Mindkind: Global mental health databank pilot

Description: 

Over the course of two years, Wellcome trust developed a pilot project which aimed at exploring the governance of data related to mental health. They joined with Sage Bionetworks to create a prototype and test the ability to build a global mental health databank (GMHD). This databank included longitudinal and electronically-derived data from youth, with an emphasis on the approaches, treatments, and interventions potentially relevant to anxiety or depression. To develop the project, a participatory data governance process was implemented involving youth aged between 14-24 in the decision-making process of the design, collection, sharing, analysis, reuse, and use of data by the researchers. The following participatory mechanisms were created: 

1. Young People’s Advisory Group: Each country had a Young People’s Advisory Group that met regularly online to discuss the study design and data collection. In total there were 32 participants from a target group of young people aged 16-24 with lived experience of mental health challenges.
2. Professional Youth Advisor: Each country recruited a full-time youth advisor as a member of the study team. Their role was to act as a link between the Young People’s Advisory Group and the project teams and support research directions. In total there were 3 professional youth advisors.
3. Global Youth Panel: The panel was composed by members with past experience on youth panels and advocacy groups for mental health issues. They met monthly to provide high-level feedback on project decisions. There were 15 participants in the panel.
4. International Youth Panel: Due to delays in the recruitment of young people for the Young People’s Advisory group, this panel was created ad hoc. The objective of this panel was to identify “active ingredients” of mental health, map data governance models and data collection strategies. 
5. Data Use Advisory Group: This group was responsible for providing scientific insights on the use of a global mental health databank and discussing its ethical issues. The members were from 7 countries (Australia, Brazil, India, Nigeria, South Africa, United Kingdom, and USA). There were 18 participants in the group.
6. Randomised Control Trial: A RCT was designed with participants from India, South Africa and the UK to test the governance conditions of the MindKind app after recording information about their mental health and behaviours. The sample was of 300 participants from a target group of young people that at least 10% had experienced mental health challenges.
7. Deliberative Democracy Sessions: Two rounds of deliberative democracy sessions were held in each country. There were in total 150 participants discussing different data governance models, benefits and concerns around data governance for a global mental health databank. 

Results: There was no statistically significant difference in enrollment rates across data governance models, even though the study expected that youngsters will prefer governance models that provide them more control over their data, they enrolled in the program regardless of the control over how their data was used or accessed. Moreover, participants given a choice of study topics showed a statistically significantly lower engagement with the study than participants who were assigned fixed study topics.

Source: 

Connected by Data. (n.d.). Connected by data | MindKind: Global mental health databank pilot. Connectedbydata.org. Retrieved May 11, 2023, from http://connectedbydata.org/cases/mindkind-global-mental-health-databank-pilot

UK

Public Acceptability of Data Sharing

Public Acceptability of Data Sharing Between the Public, Private, and Third Sectors for Research Purposes

Description: 

In 2012 the Scottish Government commissioned research to explore the public acceptability of cross-sectoral data linkage for research and statistical purposes to inform the ongoing development of a Scotland-wide Data Linkage Framework. The research indicated that the public was, in principle, broadly supportive of data linkage, particularly for health research, and of the overall objectives of the Data Linkage Framework. However, this support was conditional and a range of ambivalences and concerns were also expressed: there was significant unease about the private sector having access to public sector data and, more specifically, about the scope for commercial gain arising from data linkage. 

Report of a deliberative research project on the public’s attitudes toward data sharing. It focuses particularly on a) the public’s opinion about data sharing with the private and third sector; b) the acceptability of different methods for sharing benefits gained from the use of their data; and c) the appeal of different methods for empowering citizens in decision making about the use of their data. The study was conducted using a combination of primary and secondary research methods, comprising:

●      a desk-based literature review of international benefit-sharing models arising from the value of data sharing

●      a desk-based literature review of different methods that have been used to empower citizens in decision-making about how their data are used

●      a series of deliberative events with members of the public

Results: 

In the results, there is a dedicated section called ‘Empowering Citizens in Decision Making’. Within this section, the researchers determined the following: 

1. Participants in the discussion on public involvement in decision making regarding data sharing unanimously agreed that it was important and appropriate for the public to be involved in deciding how their data is used. 
2. When given 5 broad forms of public involvement: transparency, feedback, agenda-setting, informing policy, and representation, the most preferred formats decided by participants were transparency, feedback and informing policy.

 .      Transparency: participants wanted to know how their data are used and shared, including a rationale for why it is being shared, what type of data is being shared, how sharing works in practice and who will be able to access the data. 

 .      Feedback: this was important for informing the public about how research carried out using their data has benefited society. 

 .      Public involvement in policy-making: would allow the public, to a degree, have some control over how their data is used.

 .      Agenda-setting and representation: these were the least preferred methods as participants felt as if the public may know have the appropriate knowledge or expertise to contribute to this type of decision-making.

Source: The Scottish Government. (2013). Public Acceptability of Data Sharing Between the Public, Private and Third Sectors for Research Purposes. In The Scottish Government. https://www.google.com/url?q=https://www.gov.scot/publications/public-acceptability-data-sharing-between-public-private-third-sectors-research-purposes/&sa=D&source=docs&ust=1683813247138360&usg=AOvVaw345uiofE3iELOHd6LbG5Sz

UK

The ethical, legal, and social implications of data governance

The ethical, legal, and social implications of data governance during pandemics in the UK

Description: 

Two-week-long online citizens’ juries were organised to identify best practices for ensuring transparent, accountable, and inclusive data governance in the UK. A total of 50 citizens were recruited as jurors representing the demographic diversity of the UK population according to age, gender, ethnicity, and region. The jurors discussed data-driven technologies that played roles in the UK’s response to COVID-19, including vaccine passports, risk-scoring algorithms, and the GDPR. They were tasked with addressing the following questions: 

1. What constitutes good governance of data-driven technologies?  
2. What constitutes proportionate uses of data-driven technologies during pandemics? 

Results: 

These are the main conclusions from the jurors:

1. Transparency, communication, and clarity: there must be clear and consistent communication around the use of data-driven approaches, and the application of rules and public health measures during a pandemic.
2. Accountability: there must be an emphasis on adherence to the rule of law, protecting democracy, and ensuring robust, fair and equal enforcement of policies.
3. Equity, inclusiveness, and non-discrimination: the use of data-driven technologies should not exacerbate unequal social stratification.
4. Protection of personal freedoms: use of data-driven technologies should respect and protect individual liberties and rights.
5. Proportionate and time-limited uses: data use must balance public health needs and risks to individuals and society, and pandemic response measures must not extend into post-pandemic data futures.
6. Emergency preparedness and planning: effective, accurate, and responsibly managed data should be the basis for evidence and learning during emergency preparedness and planning.
7. Trustworthiness: the organisations and governance structures involved in the design and use of a data-driven technology must be trustworthy.

Source: Ada Lovelace Institute. (2022, July). The rule of trust. Www.adalovelaceinstitute.org. https://www.adalovelaceinstitute.org/report/trust-data-governance-pandemics/

UK

The use of data and statistics for “Public Good” in the UK

Description:

Administrative Data Research UK (ADR UK) and the Office for Statistics Regulation (OSR) partnered to explore public perceptions and understanding around the concept of what ‘public good’ means with regard to the use of data and statistics. These two organisations collaborated by developing a UK-wide public dialogue using workshops. Their overall aim was to develop a resource answering their primary question of “What are public perceptions around ‘public good’ use of data and statistics?’. Throughout these workshops, they explored the following sub-questions. 

1. How should ‘public good’ be defined and/or measured when making decisions about sharing data for research?
2. What uses of data and statistics are considered to be in the ‘public good?’
3. Are some uses of data and statistics ‘more’ in the public good than others? 
4. Are there conceptual differences between the phrases ‘public good and public interest’, public benefit, public welfare, common good, greater good, societal benefit, or other similar phrases (which are sometimes used interchangeably in the literature)?

Deliberative discussions were chosen as the method of engagement with 68 participants who lived in the UK. Four in-person workshops were held across London, Cardiff, Glasgow, and Belfast, and one workshop was held online for participants who were unable to attend in person. Additionally, an Advisory Board was created with individuals with relevant expertise to ensure important stakeholders were involved and appropriate dialogue was conducted.

Results: 

Here are the main recommendations identified by the participants:

1. Public involvement: citizens want to be involved and informed on how data in research and statistics are being used to serve the public good. Participants suggested that inclusive panels and public conversations should be held for the decision-making about data and statistics.
2. Real-world needs: citizens agree that research and statistics should aim to address the most pressing world needs, especially social inequity and social inequality. Participants recommended that the public have access to the decision-making process of Data Access Committees to understand the impact of proposed projects. 
3. Clear communication: participants recognize the importance of proactive communication that is clear and accessible that creates awareness of the importance, the motivations, and the outcomes of public good use of data for research and statistics
4. Minimize harm: data should not contribute to anything harmful, especially its use should avoid perpetuating stereotypes of certain groups of people. The public suggested consulting citizens with lived experience about potential uses of data or the interpretation of statistical patterns. Moreover, the participants agreed on the importance of increasing accountability from the experts working with data and statistics. 
5. Best practice safeguarding: citizens identified the importance of a framework, such as the Five Safes, that can be universally used to feel confident that public sector data is being used in a way they can trust. 

Source: Harkness , F., Rijneveld, C., Liu, Y., Kashef, S., & Cowan, M. (2022). A UK-wide public dialogue exploring what the public perceive as “public good” use of data for research and statistics. https://www.adruk.org/fileadmin/uploads/adruk/Documents/PE_reports_and_documents/ADR_UK_OSR_Public_Dialogue_final_report_October_2022.pdf

USA

Equitable Data Engagement and Accountability

Description: 

In its final report in April 2022— Vision for Equitable Data —the Equitable Data Working Group emphasised the need for the Federal government to use equitable data to:

1. Encourage diverse collaborations across levels of government, civil society, and the research community
2. Be accountable to the American public. By equitable data, we mean data that allow for rigorous assessment of the extent to which government programs and policies yield consistently fair, just, and impartial treatment of all individuals, including those who have been historically underserved, marginalized, and adversely affected by persistent poverty and inequality.

Fair data can bring attention to opportunities for targeted actions that will lead to noticeable improved results for marginalized communities. On essential characteristic of fair data is its disaggregation by demographic factors ( e.g., race, ethnicity, gender, language spoken, etc.), geographic factors ( e.g., rural/urban), or other variables, which allow for insights to disparities in access to and outcomes from government programs, policies and services

Establishing a robust and fair data infrastructure requires developing collaborations among various levels of government, as well as with a diverse array of external organizations, in order to advance outcomes for underserved communities. Building such infrastructure will likely need new incentives and avenues, including promoting greater data sharing and capacity building across different levels of government and expanding the research community engaged in producing and analyzing fair data. 

Moreover, it is essential to provide tools that enable civil society organizations and communities to utilize and visualize federal data and track the government’s progress towards achieving fair outcomes. This is crucial for enhancing accountability and trustworthiness with the American public.These tools should encourage community involvement in government equity initiatives, but they must be designed and implemented in a manner that aligns with the data analysis skills and resources available to community members. Ideally, these tools should allow the public to easily access meaningful and actionable data about the well-being of their communities and services provided to them. 

Source: Office of Science and Technology Policy. (2022). Request for Information; Equitable Data Engagement and Accountability (107th ed., Vol. 87, pp. 54269–54270). Federal Register. https://www.govinfo.gov/content/pkg/FR-2022-09-02/pdf/2022-19007.pdf

UK, USA, Singapore

How digital transformation can be made good for all

Description:

The aim of this project investigated the associated risks in locating digital transformation strategies outside communities, as well as their preferences and priorities when moving into a digital environment. Specifically, the project theories that top-down approaches to digital transformation and digital readiness can overlook important demographic differences in communities in areas of digital literacy, digital familiarity, access to technology, and consent. 

The project initially looks at case studies in the UK and Singapore and the characteristics of vulnerable recipients in digital transformation, critiquing top-down approaches. Secondly, it examines current examples of ‘co-creation policies’ that can be found in the Chicago police community. This allows for the identification of understanding effective methods for citizen participation and obstacles to bottom-up policy development when disabled or disadvantaged communities are included. Lastly, the concept of Living Digital Transformation (LDT) is introduced and examined in terms of how bottom-up and user-centric digitising is a more sustainable and inclusive approach, in the context of university communities. 

Source: Findlay, M., & Shanmugam, S. (2023). Participatory Digital Futures: How digital transformation can be made good for all. SSRN, 2. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430120

USA

Keating Memorial Self-Research

Description

The Open Humans Foundation is a nonprofit organisation dedicated to empowering individuals and communities around their personal data, to explore and share for the purposes of education, health, and research. The organisation operates and manages Open Humans, a project and community that enables individuals to connect their data with research and citizen science.

The Keating Memorial Self-Research encourages people to share their ideas for self-research projects with the community so different people can work on the project and share their data to contribute to the project´s analysis. The project uses a participatory research methodology, specifically a citizen science approach, to engage individuals in the collection, sharing, and analysis of personal health data. This approach involves the voluntary contribution of personal data by individuals to research studies, providing tools and resources for data management and analysis, and collaboration with researchers and other participants.

The mechanisms to engage the people in the different projects are through a monthly community call, focused on discussing topics of interest, management tools and potential collaborations, and weekly self-research chats, to discuss the self-research projects, progress, and insights. 

Through Open Humans, individuals can gain control over their personal health data, and participate in research that is aligned with their values and needs. The project emphasizes community engagement, transparency, and empowerment, and seeks to promote more inclusive and collaborative models of research.

Results: 

184 people joined the Keating Memorial project but no activity has been registered since 2021. However, the Open Humans platform has more than 30 ongoing projects related to different topics like genome, urban mobility, and different types of diseases- where people can share and explore their data. 

Source: Open Humans. (n.d.). About – Open Humans. Www.openhumans.org. Retrieved May 11, 2023, from https://www.openhumans.org/about/

USA

The Data Assembly in New York

Description: 

The Data Assembly began in the summer of 2020 with an initial focus on the response to the COVID-19 pandemic in New York City. Remote deliberations with three “mini-publics” composed of data holders, policymakers, representatives of civic rights, advocacy organisations, and citizens were held to create recommendations to guide the data-driven response to COVID-19 and other emerging threats.

Results: 

The recommendations from the discussions are the following

●      Match urgency with accountability: participants agreed that in case of a health emergency, they would accept increased surveillance but the organisations should comply with mechanisms that guarantee public accountability. 

●      Support and expand data literacy: clear communication is important for different stakeholders to understand the benefits of data reuse. 

●      Centre equity: To avoid reproducing existing inequalities, organisations should assess if their data intended to be reused misrepresent any population or can cause any harm. 

●      Engage legitimate, local actors: participants highlighted the need to include local actors, especially organisations working at the local level 

●      Develop positions for responsible data reuse: there is a need for data stewards that can help organisations manage their data and coordinate the interactions with the different stakeholders in the data sphere. 

Source: Young, A., Verhulst, S. G., Safonova, N., & Zahuranec, A. J. (2020). Responsible Data Re-Use Framework. In The Data Assembly. https://thedataassembly.org/files/nyc-data-assembly-report.pdf

Germany

Citizen Engagement and Innovative Data Use for Africa’s Development (DataCipation)

Description: 

The programme supports AU Organs in their quest to intensify citizen engagement and the role of data, digital and non-digital approaches in their programmes and initiatives. The programme is implemented in cooperation with the African Union Commission and the AU Development Agency (AUDA-NEPAD). The political partner for the programme is the Bureau of the Chairperson at the African Union Commission, demonstrating the high political commitment of the AU to the programme.

The programme takes a systemic approach, focusing on implementation across three main areas as follows:

1. Connecting policymakers with Africa’s data and digital innovators for good governance and development by enhancing the collaboration and cooperation of the AU Organs and Member States with Africa’s digital innovation ecosystem.
2. Improving citizen participation in good governance and development through innovative communications and engagement methodologies; leveraging data and digital and non-digital approaches.
3. Supporting the implementation of digital policies across Africa to improve access to meaningful participation of citizens in the digital transformation and to exploit the related potentials for social and economic development

Success factors included, enabling the African Union Commission to lead by example for digital transformation in public sector innovation as well as interactive, participatory communications efforts.Secondly it allowed for the building coalitions of the willing of AU Member States for spearheading novel digital policy approaches that pave the way for broader adoption at continental level. And lastly it permitted for a trusted partner to AUC and African policymakers by providing independent expertise geared towards realising the strategic interests including techno-geopolitical sovereignty of the African continent. 

Source: giz. (2021, December). Citizen Engagement and Innovative Data Use for Africa’s Development (DataCipation). Www.giz.de. https://www.giz.de/en/worldwide/98533.html

UK, India, South Africa

Mindkind: Global mental health databank pilot

Description: 

Over the course of two years, Wellcome trust developed a pilot project which aimed at exploring the governance of data related to mental health. They joined with Sage Bionetworks to create a prototype and test the ability to build a global mental health databank (GMHD). This databank included longitudinal and electronically-derived data from youth, with an emphasis on the approaches, treatments, and interventions potentially relevant to anxiety or depression. To develop the project, a participatory data governance process was implemented involving youth aged between 14-24 in the decision-making process of the design, collection, sharing, analysis, reuse, and use of data by the researchers. The following participatory mechanisms were created: 

1. Young People’s Advisory Group: Each country had a Young People’s Advisory Group that met regularly online to discuss the study design and data collection. In total there were 32 participants from a target group of young people aged 16-24 with lived experience of mental health challenges.
2. Professional Youth Advisor: Each country recruited a full-time youth advisor as a member of the study team. Their role was to act as a link between the Young People’s Advisory Group and the project teams and support research directions. In total there were 3 professional youth advisors.
3. Global Youth Panel: The panel was composed by members with past experience on youth panels and advocacy groups for mental health issues. They met monthly to provide high-level feedback on project decisions. There were 15 participants in the panel.
4. International Youth Panel: Due to delays in the recruitment of young people for the Young People’s Advisory group, this panel was created ad hoc. The objective of this panel was to identify “active ingredients” of mental health, map data governance models and data collection strategies. 
5. Data Use Advisory Group: This group was responsible for providing scientific insights on the use of a global mental health databank and discussing its ethical issues. The members were from 7 countries (Australia, Brazil, India, Nigeria, South Africa, United Kingdom, and USA). There were 18 participants in the group.
6. Randomised Control Trial: A RCT was designed with participants from India, South Africa and the UK to test the governance conditions of the MindKind app after recording information about their mental health and behaviours. The sample was of 300 participants from a target group of young people that at least 10% had experienced mental health challenges.
7. Deliberative Democracy Sessions: Two rounds of deliberative democracy sessions were held in each country. There were in total 150 participants discussing different data governance models, benefits and concerns around data governance for a global mental health databank. 

Results: There was no statistically significant difference in enrollment rates across data governance models, even though the study expected that youngsters will prefer governance models that provide them more control over their data, they enrolled in the program regardless of the control over how their data was used or accessed. Moreover, participants given a choice of study topics showed a statistically significantly lower engagement with the study than participants who were assigned fixed study topics.

Source: 

Connected by Data. (n.d.). Connected by data | MindKind: Global mental health databank pilot. Connectedbydata.org. Retrieved May 11, 2023, from http://connectedbydata.org/cases/mindkind-global-mental-health-databank-pilot

UK, USA, Singapore

How digital transformation can be made good for all

Description:

The aim of this project investigated the associated risks in locating digital transformation strategies outside communities, as well as their preferences and priorities when moving into a digital environment. Specifically, the project theories that top-down approaches to digital transformation and digital readiness can overlook important demographic differences in communities in areas of digital literacy, digital familiarity, access to technology, and consent. 

The project initially looks at case studies in the UK and Singapore and the characteristics of vulnerable recipients in digital transformation, critiquing top-down approaches. Secondly, it examines current examples of ‘co-creation policies’ that can be found in the Chicago police community. This allows for the identification of understanding effective methods for citizen participation and obstacles to bottom-up policy development when disabled or disadvantaged communities are included. Lastly, the concept of Living Digital Transformation (LDT) is introduced and examined in terms of how bottom-up and user-centric digitising is a more sustainable and inclusive approach, in the context of university communities. 

Source: Findlay, M., & Shanmugam, S. (2023). Participatory Digital Futures: How digital transformation can be made good for all. SSRN, 2. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430120

UK, India, South Africa

Mindkind: Global mental health databank pilot

Description: 

Over the course of two years, Wellcome trust developed a pilot project which aimed at exploring the governance of data related to mental health. They joined with Sage Bionetworks to create a prototype and test the ability to build a global mental health databank (GMHD). This databank included longitudinal and electronically-derived data from youth, with an emphasis on the approaches, treatments, and interventions potentially relevant to anxiety or depression. To develop the project, a participatory data governance process was implemented involving youth aged between 14-24 in the decision-making process of the design, collection, sharing, analysis, reuse, and use of data by the researchers. The following participatory mechanisms were created: 

1. Young People’s Advisory Group: Each country had a Young People’s Advisory Group that met regularly online to discuss the study design and data collection. In total there were 32 participants from a target group of young people aged 16-24 with lived experience of mental health challenges.
2. Professional Youth Advisor: Each country recruited a full-time youth advisor as a member of the study team. Their role was to act as a link between the Young People’s Advisory Group and the project teams and support research directions. In total there were 3 professional youth advisors.
3. Global Youth Panel: The panel was composed by members with past experience on youth panels and advocacy groups for mental health issues. They met monthly to provide high-level feedback on project decisions. There were 15 participants in the panel.
4. International Youth Panel: Due to delays in the recruitment of young people for the Young People’s Advisory group, this panel was created ad hoc. The objective of this panel was to identify “active ingredients” of mental health, map data governance models and data collection strategies. 
5. Data Use Advisory Group: This group was responsible for providing scientific insights on the use of a global mental health databank and discussing its ethical issues. The members were from 7 countries (Australia, Brazil, India, Nigeria, South Africa, United Kingdom, and USA). There were 18 participants in the group.
6. Randomised Control Trial: A RCT was designed with participants from India, South Africa and the UK to test the governance conditions of the MindKind app after recording information about their mental health and behaviours. The sample was of 300 participants from a target group of young people that at least 10% had experienced mental health challenges.
7. Deliberative Democracy Sessions: Two rounds of deliberative democracy sessions were held in each country. There were in total 150 participants discussing different data governance models, benefits and concerns around data governance for a global mental health databank. 

Results: There was no statistically significant difference in enrollment rates across data governance models, even though the study expected that youngsters will prefer governance models that provide them more control over their data, they enrolled in the program regardless of the control over how their data was used or accessed. Moreover, participants given a choice of study topics showed a statistically significantly lower engagement with the study than participants who were assigned fixed study topics.

Source: 

Connected by Data. (n.d.). Connected by data | MindKind: Global mental health databank pilot. Connectedbydata.org. Retrieved May 11, 2023, from http://connectedbydata.org/cases/mindkind-global-mental-health-databank-pilot

Spain

Digital Rights Governance Framework

Description: 

Digital technologies influence change at a fast pace in society, and might have harmful impact on individuals and communities. Against this backdrop, cities need enhanced models of governance to manage opportunities and risks driven by technology and ensure digital rights, which ultimately are human rights in the digital space, are protected and promoted. The proposal for the Digital Rights Governance Framework is a normative, yet pragmatic framework for the city-wide implementation of digital rights that unfolds the foundations, structures and tools necessary for developing a rights-based governance of the digitalisation of municipal services. It is established through (1) determining a city’s core values, (2) translating these core values into thematic areas (e.g transparency, autonomy, equity and participation), (3) combing both core values and thematic areas and choose a digital human rights understanding in the format of a bill of digital rights, a data-policy with sovereignty or a code of ethics.

Methods for public engagement and community participation:

●      Establishment of civil society groups and representatives who are willing to engage in consultations and actively participate in initiatives 

●      (Short-term projects) Arrangement of public consultations and focus groups to involve city residents which involve designing and implementing new strategies. 

●      (Long term projects) Arrangement for representatives and civil society organisations to be involved in policy developments, to ensure marginalised voices are heard. 

●      Establishment of a system for civil society organisation to promptly inform municipalities for urgent manners. 

●      Development of a comprehensive inventory of potential digital rights “violations” in the city, engage in public dialogues, and seek to put on strategies for addressing them. 

●      Community Manager to facilitate the contact with these stakeholders.

●      Organisation of public consultations on new and impactful digital policies.

Source: Cities Coalition for Digital Rights, & Un Habitat. (n.d.). DIGITAL RIGHTS GOVERNANCE FRAMEWORK. https://citiesfordigitalrights.org/sites/default/files/DIGITAL%20RIGHTS%20FRAMEWORK_CONCEPT%20FOR%20FEEDBACK.pdf

UK

Belfast to launch “Citizen Office of Digital Innovation”

Description:

In 2022, Belfast, located in Northern Ireland launched and piloted a program called Citizen Office of Digital Innovation (CODI). It aimed to increase resident engagement when it comes to data and technology. Overall, the program looks to support ‘digital citizenship skills’ which are part of the Smart Belfast Programme. This looks to help citizens to develop a better understanding of how technology is utilized in Belfast. Within the program, creative and interactive methods of engagement were used to explore concepts like co-design, citizen science, the Internet of Things, Artificial Intelligence, data, science, and privacy.  

Source: Wray, S. (Ed.). (2022, September 1). Belfast to launch “Citizen Office of Digital Innovation.” Cities Today. https://www.itu.int/hub/2022/09/belfast-to-launch-citizen-office-of-digital-innovation/

UK

Care.data

Description: 

The program care.data was made public in 2013 by the Health and Social Care Information Centre. The goal was to use data collected by GP surgeries and store them in a central database using the General Practice Extraction Service (GPES). Those who were part of GP practices were instructed that their health information would be stored in the database. Members of the GP practices who did not want their information to be uploaded were allowed to object. 

In this database, patients’ data was anonymized and could be accessed by health care researchers, managers and planners within the NHS, as well as academic institutions and organizations. The software and services used for this program were provided by Atos. 

The care.data program was heavily criticized and was deemed controversial since it was launched due to many overlooked factors. It was first critiqued around the lack of patient awareness regarding the program as well as lacking obvious options for objecting in the leaflet sent to homes. Additionally, the leaflet only described the program and the benefits surrounding it but did not include the opt-out form. 

After multiple reviews, the care.data program ended due to “major issues with project definition, schedule, budget, quality and/or benefits delivery, which at this stage do not appear to be manageable or resolvable” (Ramesh, 2015). Moreover, in December 2015 Atos was criticized by the Public Accounts Committee and was accused of taking advantage of the Department of Health. After a few years, the program ended in July 2016. 

The authors of The social license for research: why care.data ran into trouble argue that despite obtaining a lawful infrastructure for the implementation of the program, no social license was secured which led to challenges. 

Care.data could have been successful if three areas were recognized:

1. Establishing trust and confidence in the governance of research needs to go beyond focusing on economic gains and also consider the patient’s concerns as an individual seeking care. Meaning, their concerns as a citizen, who is part of a larger social fabric, is different to the concerns as an individual patient. 
2. In order for initiatives like care.data to be successful, patients must have confidence that their medical records will be securely and appropriately managed, with consideration of anonymization and public interest. 
3. Respecting the conditions of the social license involves upholding principles such as reciprocity, avoiding exploitation and prioritizing the public good.  

Source: Carter, P., Laurie, G. T., & Dixon-Woods, M. (2015). The social licence for research: whycare.dataran into trouble. Journal of Medical Ethics, 41(5), 404–409. https://doi.org/10.1136/medethics-2014-102374

Additional Links: 

●      Ramesh, R. (2015, June 26). NHS patient data plans unachievable, review finds. The Guardian. https://www.theguardian.com/politics/2015/jun/26/nhs-patient-data-plans-unachievable-review-health

●      Wikipedia . (2020, July 31). Care.data. Wikipedia. https://en.wikipedia.org/wiki/Care.data

UK

Control Access to Patient Records for Research in the UK

Description: 

The secondary use of health data for research raises complex questions of privacy and governance. Such questions are ill-suited to opinion polling where citizens must choose quickly between multiple-choice answers based on little information. Two citizens´ juries, of 17 citizens each, were convened for three days to reflect on what control informed citizens would seek over the use of health records for research. Each jury answered, “To what extent should patients control access to patient records for secondary use?”. Jurors heard from and questioned five expert witnesses. Their individual views were polled using questionnaires at the beginning and at the end of the process.

Results: 

33 out of 34 jurors voted in support of the secondary use of data for research, with 24 wanting individuals to be able to opt out, six favoring opt-in, and three voting that all records should be available without any consent process. When considering who should get access to data, both juries had very similar rationales. Both thought that public benefit was a key justification for access. Jury 1 was more strongly supportive of sharing patient records for public benefit, in contrast, jury 2 was more cautious and sought to give patients more control.

The findings show that, when informed of both risks and opportunities associated with data sharing, citizens believe an individual’s right to privacy should not prevent research that can benefit the general public. 

Source: Tully, M. P., Bozentko, K., Clement, S., Hunn, A., Hassan, L., Norris, R., Oswald, M., & Peek, N. (2018). Investigating the Extent to Which Patients Should Control Access to Patient Records for Research: A Deliberative Process Using Citizens’ Juries (2nd ed., Vol. 20). Journal of Medical Internet Research. https://www.jmir.org/2018/3/e112

UK

Explainable AI in the UK

Description: 

Two juries were selected, one in Coventry and one in Manchester, with the purpose of analysing the importance the public gives to receiving an explanation when AI was used in their healthcare, focusing on the tradeoff between the accuracy and explainability of AI systems. The jurors were made up of a cross-section of the population, representing the demographic breakdown of England as per the 2011 Census. In total, 36 individuals were selected to be jurors. They considered the tradeoff accuracy-explainability in four different scenarios: 

1. Healthcare: diagnosis of acute stroke 
2. Healthcare: finding matches between kidney transplant donors and recipients 
3. Criminal Justice: deciding which offenders should be referred to a rehabilitation programme 
4. Recruitment: screening job applications and making shortlisting decisions

Results: 

Three key themes relating to explaining AI decisions emerged from the research:

1. The importance of context for the relevance of the explanation required
2. The need for education and awareness on the use of AI for decision-making
3. The challenges in providing explanations of AI at the expense of less accurate decision-making

Most jurors felt that the relative importance of explanations and accuracy varied by context. In contexts where humans would usually provide an explanation, most jurors indicated that explanations of AI decisions should be similar to human explanations. Jurors felt this was important to help build trust and to ensure explanations were understandable.

Source: Information Commissioner’s Office. (2019). Project ExplAIn Interim Report. https://ico.org.uk/media/2615039/project-explain-20190603.pdf

UK

Fair uses of NHS patients´ data and NHS operational data

Description:

A public engagement program was held to understand what people think about the NHS allowing third parties to access their health data, ranging from academics, charities, or industries. The analysis centred on the system of rules and environment that would make a health system trustworthy. 

 The mixed methods used were:

1. Three round tables involving patient representatives in Oxford, Manchester, and London: their aim was to design the stimulus materials and the method of testing the charge question for the Citizen’s Jury. There were a total of 30 patient representatives. 
2. Citizen’s Juries in Taunton, Leeds, and London:  to discuss the question, ‘What constitutes a fair partnership between the NHS and researchers, charities and industry on uses of NHS patients’ data and NHS operational data?’. This involved 60 jurors over two and a half days.
3. A nationally representative survey from the UK: 2095 people completed the survey. It quantitatively explored important topics jurors focused on and tested broader public opinion on several key themes that emerged including the level of awareness of data access partnerships in a representative sample and aspects of communication raised by jurors. 

Results:

Specifically looking at the results around citizens’ involvement the jurors decided that citizens need to be more involved at different decision-making stages, especially in policy and practices. Meaning, citizens should be present and encouraged to participate in the process of establishing and managing data access partnerships. The jurors identified three engagement management processes that can be used to include citizens: 

1. Citizens´ Juries and deliberation for key decisions
2. Public votes to approve local partnerships
3. Playing a role in governance boards

Moreover, jurors proposed that each data access partnership should publish reports and case studies to provide transparency to the public. Moreover, jurors concluded that communities and individuals would be more resistant to data access partnerships using their data if procedures and methodologies are unclear. To increase trust, it is essential to create an awareness campaign about the national data opt-out service to increase trust and confidence in the system. 

Source: Hopkins, H., Kinsella, S., Van, A., Hopkins, M., & Mil, V. (2020). Foundations of fairness: views on uses of NHS patients’ data and NHS operational data A mixed methods public engagement programme with integrated Citizens’ Juries Findings Report. https://understandingpatientdata.org.uk/sites/default/files/2020-03/Foundations%20of%20Fairness%20-%20Full%20Research%20Report.pdf

UK, USA, Singapore

How digital transformation can be made good for all

Description:

The aim of this project investigated the associated risks in locating digital transformation strategies outside communities, as well as their preferences and priorities when moving into a digital environment. Specifically, the project theories that top-down approaches to digital transformation and digital readiness can overlook important demographic differences in communities in areas of digital literacy, digital familiarity, access to technology, and consent. 

The project initially looks at case studies in the UK and Singapore and the characteristics of vulnerable recipients in digital transformation, critiquing top-down approaches. Secondly, it examines current examples of ‘co-creation policies’ that can be found in the Chicago police community. This allows for the identification of understanding effective methods for citizen participation and obstacles to bottom-up policy development when disabled or disadvantaged communities are included. Lastly, the concept of Living Digital Transformation (LDT) is introduced and examined in terms of how bottom-up and user-centric digitising is a more sustainable and inclusive approach, in the context of university communities. 

Source: Findlay, M., & Shanmugam, S. (2023). Participatory Digital Futures: How digital transformation can be made good for all. SSRN, 2. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430120

UK, India, South Africa

Mindkind: Global mental health databank pilot

Description: 

Over the course of two years, Wellcome trust developed a pilot project which aimed at exploring the governance of data related to mental health. They joined with Sage Bionetworks to create a prototype and test the ability to build a global mental health databank (GMHD). This databank included longitudinal and electronically-derived data from youth, with an emphasis on the approaches, treatments, and interventions potentially relevant to anxiety or depression. To develop the project, a participatory data governance process was implemented involving youth aged between 14-24 in the decision-making process of the design, collection, sharing, analysis, reuse, and use of data by the researchers. The following participatory mechanisms were created: 

1. Young People’s Advisory Group: Each country had a Young People’s Advisory Group that met regularly online to discuss the study design and data collection. In total there were 32 participants from a target group of young people aged 16-24 with lived experience of mental health challenges.
2. Professional Youth Advisor: Each country recruited a full-time youth advisor as a member of the study team. Their role was to act as a link between the Young People’s Advisory Group and the project teams and support research directions. In total there were 3 professional youth advisors.
3. Global Youth Panel: The panel was composed by members with past experience on youth panels and advocacy groups for mental health issues. They met monthly to provide high-level feedback on project decisions. There were 15 participants in the panel.
4. International Youth Panel: Due to delays in the recruitment of young people for the Young People’s Advisory group, this panel was created ad hoc. The objective of this panel was to identify “active ingredients” of mental health, map data governance models and data collection strategies. 
5. Data Use Advisory Group: This group was responsible for providing scientific insights on the use of a global mental health databank and discussing its ethical issues. The members were from 7 countries (Australia, Brazil, India, Nigeria, South Africa, United Kingdom, and USA). There were 18 participants in the group.
6. Randomised Control Trial: A RCT was designed with participants from India, South Africa and the UK to test the governance conditions of the MindKind app after recording information about their mental health and behaviours. The sample was of 300 participants from a target group of young people that at least 10% had experienced mental health challenges.
7. Deliberative Democracy Sessions: Two rounds of deliberative democracy sessions were held in each country. There were in total 150 participants discussing different data governance models, benefits and concerns around data governance for a global mental health databank. 

Results: There was no statistically significant difference in enrollment rates across data governance models, even though the study expected that youngsters will prefer governance models that provide them more control over their data, they enrolled in the program regardless of the control over how their data was used or accessed. Moreover, participants given a choice of study topics showed a statistically significantly lower engagement with the study than participants who were assigned fixed study topics.

Source: 

Connected by Data. (n.d.). Connected by data | MindKind: Global mental health databank pilot. Connectedbydata.org. Retrieved May 11, 2023, from http://connectedbydata.org/cases/mindkind-global-mental-health-databank-pilot

UK

Public Acceptability of Data Sharing

Public Acceptability of Data Sharing Between the Public, Private, and Third Sectors for Research Purposes

Description: 

In 2012 the Scottish Government commissioned research to explore the public acceptability of cross-sectoral data linkage for research and statistical purposes to inform the ongoing development of a Scotland-wide Data Linkage Framework. The research indicated that the public was, in principle, broadly supportive of data linkage, particularly for health research, and of the overall objectives of the Data Linkage Framework. However, this support was conditional and a range of ambivalences and concerns were also expressed: there was significant unease about the private sector having access to public sector data and, more specifically, about the scope for commercial gain arising from data linkage. 

Report of a deliberative research project on the public’s attitudes toward data sharing. It focuses particularly on a) the public’s opinion about data sharing with the private and third sector; b) the acceptability of different methods for sharing benefits gained from the use of their data; and c) the appeal of different methods for empowering citizens in decision making about the use of their data. The study was conducted using a combination of primary and secondary research methods, comprising:

●      a desk-based literature review of international benefit-sharing models arising from the value of data sharing

●      a desk-based literature review of different methods that have been used to empower citizens in decision-making about how their data are used

●      a series of deliberative events with members of the public

Results: 

In the results, there is a dedicated section called ‘Empowering Citizens in Decision Making’. Within this section, the researchers determined the following: 

1. Participants in the discussion on public involvement in decision making regarding data sharing unanimously agreed that it was important and appropriate for the public to be involved in deciding how their data is used. 
2. When given 5 broad forms of public involvement: transparency, feedback, agenda-setting, informing policy, and representation, the most preferred formats decided by participants were transparency, feedback and informing policy.

 .      Transparency: participants wanted to know how their data are used and shared, including a rationale for why it is being shared, what type of data is being shared, how sharing works in practice and who will be able to access the data. 

 .      Feedback: this was important for informing the public about how research carried out using their data has benefited society. 

 .      Public involvement in policy-making: would allow the public, to a degree, have some control over how their data is used.

 .      Agenda-setting and representation: these were the least preferred methods as participants felt as if the public may know have the appropriate knowledge or expertise to contribute to this type of decision-making.

Source: The Scottish Government. (2013). Public Acceptability of Data Sharing Between the Public, Private and Third Sectors for Research Purposes. In The Scottish Government. https://www.google.com/url?q=https://www.gov.scot/publications/public-acceptability-data-sharing-between-public-private-third-sectors-research-purposes/&sa=D&source=docs&ust=1683813247138360&usg=AOvVaw345uiofE3iELOHd6LbG5Sz

UK

The ethical, legal, and social implications of data governance

The ethical, legal, and social implications of data governance during pandemics in the UK

Description: 

Two-week-long online citizens’ juries were organised to identify best practices for ensuring transparent, accountable, and inclusive data governance in the UK. A total of 50 citizens were recruited as jurors representing the demographic diversity of the UK population according to age, gender, ethnicity, and region. The jurors discussed data-driven technologies that played roles in the UK’s response to COVID-19, including vaccine passports, risk-scoring algorithms, and the GDPR. They were tasked with addressing the following questions: 

1. What constitutes good governance of data-driven technologies?  
2. What constitutes proportionate uses of data-driven technologies during pandemics? 

Results: 

These are the main conclusions from the jurors:

1. Transparency, communication, and clarity: there must be clear and consistent communication around the use of data-driven approaches, and the application of rules and public health measures during a pandemic.
2. Accountability: there must be an emphasis on adherence to the rule of law, protecting democracy, and ensuring robust, fair and equal enforcement of policies.
3. Equity, inclusiveness, and non-discrimination: the use of data-driven technologies should not exacerbate unequal social stratification.
4. Protection of personal freedoms: use of data-driven technologies should respect and protect individual liberties and rights.
5. Proportionate and time-limited uses: data use must balance public health needs and risks to individuals and society, and pandemic response measures must not extend into post-pandemic data futures.
6. Emergency preparedness and planning: effective, accurate, and responsibly managed data should be the basis for evidence and learning during emergency preparedness and planning.
7. Trustworthiness: the organisations and governance structures involved in the design and use of a data-driven technology must be trustworthy.

Source: Ada Lovelace Institute. (2022, July). The rule of trust. Www.adalovelaceinstitute.org. https://www.adalovelaceinstitute.org/report/trust-data-governance-pandemics/

UK

The use of data and statistics for “Public Good” in the UK

Description:

Administrative Data Research UK (ADR UK) and the Office for Statistics Regulation (OSR) partnered to explore public perceptions and understanding around the concept of what ‘public good’ means with regard to the use of data and statistics. These two organisations collaborated by developing a UK-wide public dialogue using workshops. Their overall aim was to develop a resource answering their primary question of “What are public perceptions around ‘public good’ use of data and statistics?’. Throughout these workshops, they explored the following sub-questions. 

1. How should ‘public good’ be defined and/or measured when making decisions about sharing data for research?
2. What uses of data and statistics are considered to be in the ‘public good?’
3. Are some uses of data and statistics ‘more’ in the public good than others? 
4. Are there conceptual differences between the phrases ‘public good and public interest’, public benefit, public welfare, common good, greater good, societal benefit, or other similar phrases (which are sometimes used interchangeably in the literature)?

Deliberative discussions were chosen as the method of engagement with 68 participants who lived in the UK. Four in-person workshops were held across London, Cardiff, Glasgow, and Belfast, and one workshop was held online for participants who were unable to attend in person. Additionally, an Advisory Board was created with individuals with relevant expertise to ensure important stakeholders were involved and appropriate dialogue was conducted.

Results: 

Here are the main recommendations identified by the participants:

1. Public involvement: citizens want to be involved and informed on how data in research and statistics are being used to serve the public good. Participants suggested that inclusive panels and public conversations should be held for the decision-making about data and statistics.
2. Real-world needs: citizens agree that research and statistics should aim to address the most pressing world needs, especially social inequity and social inequality. Participants recommended that the public have access to the decision-making process of Data Access Committees to understand the impact of proposed projects. 
3. Clear communication: participants recognize the importance of proactive communication that is clear and accessible that creates awareness of the importance, the motivations, and the outcomes of public good use of data for research and statistics
4. Minimize harm: data should not contribute to anything harmful, especially its use should avoid perpetuating stereotypes of certain groups of people. The public suggested consulting citizens with lived experience about potential uses of data or the interpretation of statistical patterns. Moreover, the participants agreed on the importance of increasing accountability from the experts working with data and statistics. 
5. Best practice safeguarding: citizens identified the importance of a framework, such as the Five Safes, that can be universally used to feel confident that public sector data is being used in a way they can trust. 

Source: Harkness , F., Rijneveld, C., Liu, Y., Kashef, S., & Cowan, M. (2022). A UK-wide public dialogue exploring what the public perceive as “public good” use of data for research and statistics. https://www.adruk.org/fileadmin/uploads/adruk/Documents/PE_reports_and_documents/ADR_UK_OSR_Public_Dialogue_final_report_October_2022.pdf

USA

Equitable Data Engagement and Accountability

Description: 

In its final report in April 2022— Vision for Equitable Data —the Equitable Data Working Group emphasised the need for the Federal government to use equitable data to:

1. Encourage diverse collaborations across levels of government, civil society, and the research community
2. Be accountable to the American public. By equitable data, we mean data that allow for rigorous assessment of the extent to which government programs and policies yield consistently fair, just, and impartial treatment of all individuals, including those who have been historically underserved, marginalized, and adversely affected by persistent poverty and inequality.

Fair data can bring attention to opportunities for targeted actions that will lead to noticeable improved results for marginalized communities. On essential characteristic of fair data is its disaggregation by demographic factors ( e.g., race, ethnicity, gender, language spoken, etc.), geographic factors ( e.g., rural/urban), or other variables, which allow for insights to disparities in access to and outcomes from government programs, policies and services

Establishing a robust and fair data infrastructure requires developing collaborations among various levels of government, as well as with a diverse array of external organizations, in order to advance outcomes for underserved communities. Building such infrastructure will likely need new incentives and avenues, including promoting greater data sharing and capacity building across different levels of government and expanding the research community engaged in producing and analyzing fair data. 

Moreover, it is essential to provide tools that enable civil society organizations and communities to utilize and visualize federal data and track the government’s progress towards achieving fair outcomes. This is crucial for enhancing accountability and trustworthiness with the American public.These tools should encourage community involvement in government equity initiatives, but they must be designed and implemented in a manner that aligns with the data analysis skills and resources available to community members. Ideally, these tools should allow the public to easily access meaningful and actionable data about the well-being of their communities and services provided to them. 

Source: Office of Science and Technology Policy. (2022). Request for Information; Equitable Data Engagement and Accountability (107th ed., Vol. 87, pp. 54269–54270). Federal Register. https://www.govinfo.gov/content/pkg/FR-2022-09-02/pdf/2022-19007.pdf

UK, USA, Singapore

How digital transformation can be made good for all

Description:

The aim of this project investigated the associated risks in locating digital transformation strategies outside communities, as well as their preferences and priorities when moving into a digital environment. Specifically, the project theories that top-down approaches to digital transformation and digital readiness can overlook important demographic differences in communities in areas of digital literacy, digital familiarity, access to technology, and consent. 

The project initially looks at case studies in the UK and Singapore and the characteristics of vulnerable recipients in digital transformation, critiquing top-down approaches. Secondly, it examines current examples of ‘co-creation policies’ that can be found in the Chicago police community. This allows for the identification of understanding effective methods for citizen participation and obstacles to bottom-up policy development when disabled or disadvantaged communities are included. Lastly, the concept of Living Digital Transformation (LDT) is introduced and examined in terms of how bottom-up and user-centric digitising is a more sustainable and inclusive approach, in the context of university communities. 

Source: Findlay, M., & Shanmugam, S. (2023). Participatory Digital Futures: How digital transformation can be made good for all. SSRN, 2. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430120

USA

Keating Memorial Self-Research

Description

The Open Humans Foundation is a nonprofit organisation dedicated to empowering individuals and communities around their personal data, to explore and share for the purposes of education, health, and research. The organisation operates and manages Open Humans, a project and community that enables individuals to connect their data with research and citizen science.

The Keating Memorial Self-Research encourages people to share their ideas for self-research projects with the community so different people can work on the project and share their data to contribute to the project´s analysis. The project uses a participatory research methodology, specifically a citizen science approach, to engage individuals in the collection, sharing, and analysis of personal health data. This approach involves the voluntary contribution of personal data by individuals to research studies, providing tools and resources for data management and analysis, and collaboration with researchers and other participants.

The mechanisms to engage the people in the different projects are through a monthly community call, focused on discussing topics of interest, management tools and potential collaborations, and weekly self-research chats, to discuss the self-research projects, progress, and insights. 

Through Open Humans, individuals can gain control over their personal health data, and participate in research that is aligned with their values and needs. The project emphasizes community engagement, transparency, and empowerment, and seeks to promote more inclusive and collaborative models of research.

Results: 

184 people joined the Keating Memorial project but no activity has been registered since 2021. However, the Open Humans platform has more than 30 ongoing projects related to different topics like genome, urban mobility, and different types of diseases- where people can share and explore their data. 

Source: Open Humans. (n.d.). About – Open Humans. Www.openhumans.org. Retrieved May 11, 2023, from https://www.openhumans.org/about/

USA

The Data Assembly in New York

Description: 

The Data Assembly began in the summer of 2020 with an initial focus on the response to the COVID-19 pandemic in New York City. Remote deliberations with three “mini-publics” composed of data holders, policymakers, representatives of civic rights, advocacy organisations, and citizens were held to create recommendations to guide the data-driven response to COVID-19 and other emerging threats.

Results: 

The recommendations from the discussions are the following

●      Match urgency with accountability: participants agreed that in case of a health emergency, they would accept increased surveillance but the organisations should comply with mechanisms that guarantee public accountability. 

●      Support and expand data literacy: clear communication is important for different stakeholders to understand the benefits of data reuse. 

●      Centre equity: To avoid reproducing existing inequalities, organisations should assess if their data intended to be reused misrepresent any population or can cause any harm. 

●      Engage legitimate, local actors: participants highlighted the need to include local actors, especially organisations working at the local level 

●      Develop positions for responsible data reuse: there is a need for data stewards that can help organisations manage their data and coordinate the interactions with the different stakeholders in the data sphere. 

Source: Young, A., Verhulst, S. G., Safonova, N., & Zahuranec, A. J. (2020). Responsible Data Re-Use Framework. In The Data Assembly. https://thedataassembly.org/files/nyc-data-assembly-report.pdf

UK

Belfast to launch “Citizen Office of Digital Innovation”

Description:

In 2022, Belfast, located in Northern Ireland launched and piloted a program called Citizen Office of Digital Innovation (CODI). It aimed to increase resident engagement when it comes to data and technology. Overall, the program looks to support ‘digital citizenship skills’ which are part of the Smart Belfast Programme. This looks to help citizens to develop a better understanding of how technology is utilized in Belfast. Within the program, creative and interactive methods of engagement were used to explore concepts like co-design, citizen science, the Internet of Things, Artificial Intelligence, data, science, and privacy.  

Source: Wray, S. (Ed.). (2022, September 1). Belfast to launch “Citizen Office of Digital Innovation.” Cities Today. https://www.itu.int/hub/2022/09/belfast-to-launch-citizen-office-of-digital-innovation/

UK

Care.data

Description: 

The program care.data was made public in 2013 by the Health and Social Care Information Centre. The goal was to use data collected by GP surgeries and store them in a central database using the General Practice Extraction Service (GPES). Those who were part of GP practices were instructed that their health information would be stored in the database. Members of the GP practices who did not want their information to be uploaded were allowed to object. 

In this database, patients’ data was anonymized and could be accessed by health care researchers, managers and planners within the NHS, as well as academic institutions and organizations. The software and services used for this program were provided by Atos. 

The care.data program was heavily criticized and was deemed controversial since it was launched due to many overlooked factors. It was first critiqued around the lack of patient awareness regarding the program as well as lacking obvious options for objecting in the leaflet sent to homes. Additionally, the leaflet only described the program and the benefits surrounding it but did not include the opt-out form. 

After multiple reviews, the care.data program ended due to “major issues with project definition, schedule, budget, quality and/or benefits delivery, which at this stage do not appear to be manageable or resolvable” (Ramesh, 2015). Moreover, in December 2015 Atos was criticized by the Public Accounts Committee and was accused of taking advantage of the Department of Health. After a few years, the program ended in July 2016. 

The authors of The social license for research: why care.data ran into trouble argue that despite obtaining a lawful infrastructure for the implementation of the program, no social license was secured which led to challenges. 

Care.data could have been successful if three areas were recognized:

1. Establishing trust and confidence in the governance of research needs to go beyond focusing on economic gains and also consider the patient’s concerns as an individual seeking care. Meaning, their concerns as a citizen, who is part of a larger social fabric, is different to the concerns as an individual patient. 
2. In order for initiatives like care.data to be successful, patients must have confidence that their medical records will be securely and appropriately managed, with consideration of anonymization and public interest. 
3. Respecting the conditions of the social license involves upholding principles such as reciprocity, avoiding exploitation and prioritizing the public good.  

Source: Carter, P., Laurie, G. T., & Dixon-Woods, M. (2015). The social licence for research: whycare.dataran into trouble. Journal of Medical Ethics, 41(5), 404–409. https://doi.org/10.1136/medethics-2014-102374

Additional Links: 

●      Ramesh, R. (2015, June 26). NHS patient data plans unachievable, review finds. The Guardian. https://www.theguardian.com/politics/2015/jun/26/nhs-patient-data-plans-unachievable-review-health

●      Wikipedia . (2020, July 31). Care.data. Wikipedia. https://en.wikipedia.org/wiki/Care.data

UK

Explainable AI in the UK

Description: 

Two juries were selected, one in Coventry and one in Manchester, with the purpose of analysing the importance the public gives to receiving an explanation when AI was used in their healthcare, focusing on the tradeoff between the accuracy and explainability of AI systems. The jurors were made up of a cross-section of the population, representing the demographic breakdown of England as per the 2011 Census. In total, 36 individuals were selected to be jurors. They considered the tradeoff accuracy-explainability in four different scenarios: 

1. Healthcare: diagnosis of acute stroke 
2. Healthcare: finding matches between kidney transplant donors and recipients 
3. Criminal Justice: deciding which offenders should be referred to a rehabilitation programme 
4. Recruitment: screening job applications and making shortlisting decisions

Results: 

Three key themes relating to explaining AI decisions emerged from the research:

1. The importance of context for the relevance of the explanation required
2. The need for education and awareness on the use of AI for decision-making
3. The challenges in providing explanations of AI at the expense of less accurate decision-making

Most jurors felt that the relative importance of explanations and accuracy varied by context. In contexts where humans would usually provide an explanation, most jurors indicated that explanations of AI decisions should be similar to human explanations. Jurors felt this was important to help build trust and to ensure explanations were understandable.

Source: Information Commissioner’s Office. (2019). Project ExplAIn Interim Report. https://ico.org.uk/media/2615039/project-explain-20190603.pdf

UK

Fair uses of NHS patients´ data and NHS operational data

Description:

A public engagement program was held to understand what people think about the NHS allowing third parties to access their health data, ranging from academics, charities, or industries. The analysis centred on the system of rules and environment that would make a health system trustworthy. 

 The mixed methods used were:

1. Three round tables involving patient representatives in Oxford, Manchester, and London: their aim was to design the stimulus materials and the method of testing the charge question for the Citizen’s Jury. There were a total of 30 patient representatives. 
2. Citizen’s Juries in Taunton, Leeds, and London:  to discuss the question, ‘What constitutes a fair partnership between the NHS and researchers, charities and industry on uses of NHS patients’ data and NHS operational data?’. This involved 60 jurors over two and a half days.
3. A nationally representative survey from the UK: 2095 people completed the survey. It quantitatively explored important topics jurors focused on and tested broader public opinion on several key themes that emerged including the level of awareness of data access partnerships in a representative sample and aspects of communication raised by jurors. 

Results:

Specifically looking at the results around citizens’ involvement the jurors decided that citizens need to be more involved at different decision-making stages, especially in policy and practices. Meaning, citizens should be present and encouraged to participate in the process of establishing and managing data access partnerships. The jurors identified three engagement management processes that can be used to include citizens: 

1. Citizens´ Juries and deliberation for key decisions
2. Public votes to approve local partnerships
3. Playing a role in governance boards

Moreover, jurors proposed that each data access partnership should publish reports and case studies to provide transparency to the public. Moreover, jurors concluded that communities and individuals would be more resistant to data access partnerships using their data if procedures and methodologies are unclear. To increase trust, it is essential to create an awareness campaign about the national data opt-out service to increase trust and confidence in the system. 

Source: Hopkins, H., Kinsella, S., Van, A., Hopkins, M., & Mil, V. (2020). Foundations of fairness: views on uses of NHS patients’ data and NHS operational data A mixed methods public engagement programme with integrated Citizens’ Juries Findings Report. https://understandingpatientdata.org.uk/sites/default/files/2020-03/Foundations%20of%20Fairness%20-%20Full%20Research%20Report.pdf

UK

Public Acceptability of Data Sharing

Public Acceptability of Data Sharing Between the Public, Private, and Third Sectors for Research Purposes

Description: 

In 2012 the Scottish Government commissioned research to explore the public acceptability of cross-sectoral data linkage for research and statistical purposes to inform the ongoing development of a Scotland-wide Data Linkage Framework. The research indicated that the public was, in principle, broadly supportive of data linkage, particularly for health research, and of the overall objectives of the Data Linkage Framework. However, this support was conditional and a range of ambivalences and concerns were also expressed: there was significant unease about the private sector having access to public sector data and, more specifically, about the scope for commercial gain arising from data linkage. 

Report of a deliberative research project on the public’s attitudes toward data sharing. It focuses particularly on a) the public’s opinion about data sharing with the private and third sector; b) the acceptability of different methods for sharing benefits gained from the use of their data; and c) the appeal of different methods for empowering citizens in decision making about the use of their data. The study was conducted using a combination of primary and secondary research methods, comprising:

●      a desk-based literature review of international benefit-sharing models arising from the value of data sharing

●      a desk-based literature review of different methods that have been used to empower citizens in decision-making about how their data are used

●      a series of deliberative events with members of the public

Results: 

In the results, there is a dedicated section called ‘Empowering Citizens in Decision Making’. Within this section, the researchers determined the following: 

1. Participants in the discussion on public involvement in decision making regarding data sharing unanimously agreed that it was important and appropriate for the public to be involved in deciding how their data is used. 
2. When given 5 broad forms of public involvement: transparency, feedback, agenda-setting, informing policy, and representation, the most preferred formats decided by participants were transparency, feedback and informing policy.

 .      Transparency: participants wanted to know how their data are used and shared, including a rationale for why it is being shared, what type of data is being shared, how sharing works in practice and who will be able to access the data. 

 .      Feedback: this was important for informing the public about how research carried out using their data has benefited society. 

 .      Public involvement in policy-making: would allow the public, to a degree, have some control over how their data is used.

 .      Agenda-setting and representation: these were the least preferred methods as participants felt as if the public may know have the appropriate knowledge or expertise to contribute to this type of decision-making.

Source: The Scottish Government. (2013). Public Acceptability of Data Sharing Between the Public, Private and Third Sectors for Research Purposes. In The Scottish Government. https://www.google.com/url?q=https://www.gov.scot/publications/public-acceptability-data-sharing-between-public-private-third-sectors-research-purposes/&sa=D&source=docs&ust=1683813247138360&usg=AOvVaw345uiofE3iELOHd6LbG5Sz

USA

The Data Assembly in New York

Description: 

The Data Assembly began in the summer of 2020 with an initial focus on the response to the COVID-19 pandemic in New York City. Remote deliberations with three “mini-publics” composed of data holders, policymakers, representatives of civic rights, advocacy organisations, and citizens were held to create recommendations to guide the data-driven response to COVID-19 and other emerging threats.

Results: 

The recommendations from the discussions are the following

●      Match urgency with accountability: participants agreed that in case of a health emergency, they would accept increased surveillance but the organisations should comply with mechanisms that guarantee public accountability. 

●      Support and expand data literacy: clear communication is important for different stakeholders to understand the benefits of data reuse. 

●      Centre equity: To avoid reproducing existing inequalities, organisations should assess if their data intended to be reused misrepresent any population or can cause any harm. 

●      Engage legitimate, local actors: participants highlighted the need to include local actors, especially organisations working at the local level 

●      Develop positions for responsible data reuse: there is a need for data stewards that can help organisations manage their data and coordinate the interactions with the different stakeholders in the data sphere. 

Source: Young, A., Verhulst, S. G., Safonova, N., & Zahuranec, A. J. (2020). Responsible Data Re-Use Framework. In The Data Assembly. https://thedataassembly.org/files/nyc-data-assembly-report.pdf

UK

The use of data and statistics for “Public Good” in the UK

Description:

Administrative Data Research UK (ADR UK) and the Office for Statistics Regulation (OSR) partnered to explore public perceptions and understanding around the concept of what ‘public good’ means with regard to the use of data and statistics. These two organisations collaborated by developing a UK-wide public dialogue using workshops. Their overall aim was to develop a resource answering their primary question of “What are public perceptions around ‘public good’ use of data and statistics?’. Throughout these workshops, they explored the following sub-questions. 

1. How should ‘public good’ be defined and/or measured when making decisions about sharing data for research?
2. What uses of data and statistics are considered to be in the ‘public good?’
3. Are some uses of data and statistics ‘more’ in the public good than others? 
4. Are there conceptual differences between the phrases ‘public good and public interest’, public benefit, public welfare, common good, greater good, societal benefit, or other similar phrases (which are sometimes used interchangeably in the literature)?

Deliberative discussions were chosen as the method of engagement with 68 participants who lived in the UK. Four in-person workshops were held across London, Cardiff, Glasgow, and Belfast, and one workshop was held online for participants who were unable to attend in person. Additionally, an Advisory Board was created with individuals with relevant expertise to ensure important stakeholders were involved and appropriate dialogue was conducted.

Results: 

Here are the main recommendations identified by the participants:

1. Public involvement: citizens want to be involved and informed on how data in research and statistics are being used to serve the public good. Participants suggested that inclusive panels and public conversations should be held for the decision-making about data and statistics.
2. Real-world needs: citizens agree that research and statistics should aim to address the most pressing world needs, especially social inequity and social inequality. Participants recommended that the public have access to the decision-making process of Data Access Committees to understand the impact of proposed projects. 
3. Clear communication: participants recognize the importance of proactive communication that is clear and accessible that creates awareness of the importance, the motivations, and the outcomes of public good use of data for research and statistics
4. Minimize harm: data should not contribute to anything harmful, especially its use should avoid perpetuating stereotypes of certain groups of people. The public suggested consulting citizens with lived experience about potential uses of data or the interpretation of statistical patterns. Moreover, the participants agreed on the importance of increasing accountability from the experts working with data and statistics. 
5. Best practice safeguarding: citizens identified the importance of a framework, such as the Five Safes, that can be universally used to feel confident that public sector data is being used in a way they can trust. 

Source: Harkness , F., Rijneveld, C., Liu, Y., Kashef, S., & Cowan, M. (2022). A UK-wide public dialogue exploring what the public perceive as “public good” use of data for research and statistics. https://www.adruk.org/fileadmin/uploads/adruk/Documents/PE_reports_and_documents/ADR_UK_OSR_Public_Dialogue_final_report_October_2022.pdf

UK

Control Access to Patient Records for Research in the UK

Description: 

The secondary use of health data for research raises complex questions of privacy and governance. Such questions are ill-suited to opinion polling where citizens must choose quickly between multiple-choice answers based on little information. Two citizens´ juries, of 17 citizens each, were convened for three days to reflect on what control informed citizens would seek over the use of health records for research. Each jury answered, “To what extent should patients control access to patient records for secondary use?”. Jurors heard from and questioned five expert witnesses. Their individual views were polled using questionnaires at the beginning and at the end of the process.

Results: 

33 out of 34 jurors voted in support of the secondary use of data for research, with 24 wanting individuals to be able to opt out, six favoring opt-in, and three voting that all records should be available without any consent process. When considering who should get access to data, both juries had very similar rationales. Both thought that public benefit was a key justification for access. Jury 1 was more strongly supportive of sharing patient records for public benefit, in contrast, jury 2 was more cautious and sought to give patients more control.

The findings show that, when informed of both risks and opportunities associated with data sharing, citizens believe an individual’s right to privacy should not prevent research that can benefit the general public. 

Source: Tully, M. P., Bozentko, K., Clement, S., Hunn, A., Hassan, L., Norris, R., Oswald, M., & Peek, N. (2018). Investigating the Extent to Which Patients Should Control Access to Patient Records for Research: A Deliberative Process Using Citizens’ Juries (2nd ed., Vol. 20). Journal of Medical Internet Research. https://www.jmir.org/2018/3/e112

UK

Explainable AI in the UK

Description: 

Two juries were selected, one in Coventry and one in Manchester, with the purpose of analysing the importance the public gives to receiving an explanation when AI was used in their healthcare, focusing on the tradeoff between the accuracy and explainability of AI systems. The jurors were made up of a cross-section of the population, representing the demographic breakdown of England as per the 2011 Census. In total, 36 individuals were selected to be jurors. They considered the tradeoff accuracy-explainability in four different scenarios: 

1. Healthcare: diagnosis of acute stroke 
2. Healthcare: finding matches between kidney transplant donors and recipients 
3. Criminal Justice: deciding which offenders should be referred to a rehabilitation programme 
4. Recruitment: screening job applications and making shortlisting decisions

Results: 

Three key themes relating to explaining AI decisions emerged from the research:

1. The importance of context for the relevance of the explanation required
2. The need for education and awareness on the use of AI for decision-making
3. The challenges in providing explanations of AI at the expense of less accurate decision-making

Most jurors felt that the relative importance of explanations and accuracy varied by context. In contexts where humans would usually provide an explanation, most jurors indicated that explanations of AI decisions should be similar to human explanations. Jurors felt this was important to help build trust and to ensure explanations were understandable.

Source: Information Commissioner’s Office. (2019). Project ExplAIn Interim Report. https://ico.org.uk/media/2615039/project-explain-20190603.pdf

UK

Fair uses of NHS patients´ data and NHS operational data

Description:

A public engagement program was held to understand what people think about the NHS allowing third parties to access their health data, ranging from academics, charities, or industries. The analysis centred on the system of rules and environment that would make a health system trustworthy. 

 The mixed methods used were:

1. Three round tables involving patient representatives in Oxford, Manchester, and London: their aim was to design the stimulus materials and the method of testing the charge question for the Citizen’s Jury. There were a total of 30 patient representatives. 
2. Citizen’s Juries in Taunton, Leeds, and London:  to discuss the question, ‘What constitutes a fair partnership between the NHS and researchers, charities and industry on uses of NHS patients’ data and NHS operational data?’. This involved 60 jurors over two and a half days.
3. A nationally representative survey from the UK: 2095 people completed the survey. It quantitatively explored important topics jurors focused on and tested broader public opinion on several key themes that emerged including the level of awareness of data access partnerships in a representative sample and aspects of communication raised by jurors. 

Results:

Specifically looking at the results around citizens’ involvement the jurors decided that citizens need to be more involved at different decision-making stages, especially in policy and practices. Meaning, citizens should be present and encouraged to participate in the process of establishing and managing data access partnerships. The jurors identified three engagement management processes that can be used to include citizens: 

1. Citizens´ Juries and deliberation for key decisions
2. Public votes to approve local partnerships
3. Playing a role in governance boards

Moreover, jurors proposed that each data access partnership should publish reports and case studies to provide transparency to the public. Moreover, jurors concluded that communities and individuals would be more resistant to data access partnerships using their data if procedures and methodologies are unclear. To increase trust, it is essential to create an awareness campaign about the national data opt-out service to increase trust and confidence in the system. 

Source: Hopkins, H., Kinsella, S., Van, A., Hopkins, M., & Mil, V. (2020). Foundations of fairness: views on uses of NHS patients’ data and NHS operational data A mixed methods public engagement programme with integrated Citizens’ Juries Findings Report. https://understandingpatientdata.org.uk/sites/default/files/2020-03/Foundations%20of%20Fairness%20-%20Full%20Research%20Report.pdf

UK, USA, Singapore

How digital transformation can be made good for all

Description:

The aim of this project investigated the associated risks in locating digital transformation strategies outside communities, as well as their preferences and priorities when moving into a digital environment. Specifically, the project theories that top-down approaches to digital transformation and digital readiness can overlook important demographic differences in communities in areas of digital literacy, digital familiarity, access to technology, and consent. 

The project initially looks at case studies in the UK and Singapore and the characteristics of vulnerable recipients in digital transformation, critiquing top-down approaches. Secondly, it examines current examples of ‘co-creation policies’ that can be found in the Chicago police community. This allows for the identification of understanding effective methods for citizen participation and obstacles to bottom-up policy development when disabled or disadvantaged communities are included. Lastly, the concept of Living Digital Transformation (LDT) is introduced and examined in terms of how bottom-up and user-centric digitising is a more sustainable and inclusive approach, in the context of university communities. 

Source: Findlay, M., & Shanmugam, S. (2023). Participatory Digital Futures: How digital transformation can be made good for all. SSRN, 2. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430120

USA

Keating Memorial Self-Research

Description

The Open Humans Foundation is a nonprofit organisation dedicated to empowering individuals and communities around their personal data, to explore and share for the purposes of education, health, and research. The organisation operates and manages Open Humans, a project and community that enables individuals to connect their data with research and citizen science.

The Keating Memorial Self-Research encourages people to share their ideas for self-research projects with the community so different people can work on the project and share their data to contribute to the project´s analysis. The project uses a participatory research methodology, specifically a citizen science approach, to engage individuals in the collection, sharing, and analysis of personal health data. This approach involves the voluntary contribution of personal data by individuals to research studies, providing tools and resources for data management and analysis, and collaboration with researchers and other participants.

The mechanisms to engage the people in the different projects are through a monthly community call, focused on discussing topics of interest, management tools and potential collaborations, and weekly self-research chats, to discuss the self-research projects, progress, and insights. 

Through Open Humans, individuals can gain control over their personal health data, and participate in research that is aligned with their values and needs. The project emphasizes community engagement, transparency, and empowerment, and seeks to promote more inclusive and collaborative models of research.

Results: 

184 people joined the Keating Memorial project but no activity has been registered since 2021. However, the Open Humans platform has more than 30 ongoing projects related to different topics like genome, urban mobility, and different types of diseases- where people can share and explore their data. 

Source: Open Humans. (n.d.). About – Open Humans. Www.openhumans.org. Retrieved May 11, 2023, from https://www.openhumans.org/about/

UK, India, South Africa

Mindkind: Global mental health databank pilot

Description: 

Over the course of two years, Wellcome trust developed a pilot project which aimed at exploring the governance of data related to mental health. They joined with Sage Bionetworks to create a prototype and test the ability to build a global mental health databank (GMHD). This databank included longitudinal and electronically-derived data from youth, with an emphasis on the approaches, treatments, and interventions potentially relevant to anxiety or depression. To develop the project, a participatory data governance process was implemented involving youth aged between 14-24 in the decision-making process of the design, collection, sharing, analysis, reuse, and use of data by the researchers. The following participatory mechanisms were created: 

1. Young People’s Advisory Group: Each country had a Young People’s Advisory Group that met regularly online to discuss the study design and data collection. In total there were 32 participants from a target group of young people aged 16-24 with lived experience of mental health challenges.
2. Professional Youth Advisor: Each country recruited a full-time youth advisor as a member of the study team. Their role was to act as a link between the Young People’s Advisory Group and the project teams and support research directions. In total there were 3 professional youth advisors.
3. Global Youth Panel: The panel was composed by members with past experience on youth panels and advocacy groups for mental health issues. They met monthly to provide high-level feedback on project decisions. There were 15 participants in the panel.
4. International Youth Panel: Due to delays in the recruitment of young people for the Young People’s Advisory group, this panel was created ad hoc. The objective of this panel was to identify “active ingredients” of mental health, map data governance models and data collection strategies. 
5. Data Use Advisory Group: This group was responsible for providing scientific insights on the use of a global mental health databank and discussing its ethical issues. The members were from 7 countries (Australia, Brazil, India, Nigeria, South Africa, United Kingdom, and USA). There were 18 participants in the group.
6. Randomised Control Trial: A RCT was designed with participants from India, South Africa and the UK to test the governance conditions of the MindKind app after recording information about their mental health and behaviours. The sample was of 300 participants from a target group of young people that at least 10% had experienced mental health challenges.
7. Deliberative Democracy Sessions: Two rounds of deliberative democracy sessions were held in each country. There were in total 150 participants discussing different data governance models, benefits and concerns around data governance for a global mental health databank. 

Results: There was no statistically significant difference in enrollment rates across data governance models, even though the study expected that youngsters will prefer governance models that provide them more control over their data, they enrolled in the program regardless of the control over how their data was used or accessed. Moreover, participants given a choice of study topics showed a statistically significantly lower engagement with the study than participants who were assigned fixed study topics.

Source: 

Connected by Data. (n.d.). Connected by data | MindKind: Global mental health databank pilot. Connectedbydata.org. Retrieved May 11, 2023, from http://connectedbydata.org/cases/mindkind-global-mental-health-databank-pilot

UK

Public Acceptability of Data Sharing

Public Acceptability of Data Sharing Between the Public, Private, and Third Sectors for Research Purposes

Description: 

In 2012 the Scottish Government commissioned research to explore the public acceptability of cross-sectoral data linkage for research and statistical purposes to inform the ongoing development of a Scotland-wide Data Linkage Framework. The research indicated that the public was, in principle, broadly supportive of data linkage, particularly for health research, and of the overall objectives of the Data Linkage Framework. However, this support was conditional and a range of ambivalences and concerns were also expressed: there was significant unease about the private sector having access to public sector data and, more specifically, about the scope for commercial gain arising from data linkage. 

Report of a deliberative research project on the public’s attitudes toward data sharing. It focuses particularly on a) the public’s opinion about data sharing with the private and third sector; b) the acceptability of different methods for sharing benefits gained from the use of their data; and c) the appeal of different methods for empowering citizens in decision making about the use of their data. The study was conducted using a combination of primary and secondary research methods, comprising:

●      a desk-based literature review of international benefit-sharing models arising from the value of data sharing

●      a desk-based literature review of different methods that have been used to empower citizens in decision-making about how their data are used

●      a series of deliberative events with members of the public

Results: 

In the results, there is a dedicated section called ‘Empowering Citizens in Decision Making’. Within this section, the researchers determined the following: 

1. Participants in the discussion on public involvement in decision making regarding data sharing unanimously agreed that it was important and appropriate for the public to be involved in deciding how their data is used. 
2. When given 5 broad forms of public involvement: transparency, feedback, agenda-setting, informing policy, and representation, the most preferred formats decided by participants were transparency, feedback and informing policy.

 .      Transparency: participants wanted to know how their data are used and shared, including a rationale for why it is being shared, what type of data is being shared, how sharing works in practice and who will be able to access the data. 

 .      Feedback: this was important for informing the public about how research carried out using their data has benefited society. 

 .      Public involvement in policy-making: would allow the public, to a degree, have some control over how their data is used.

 .      Agenda-setting and representation: these were the least preferred methods as participants felt as if the public may know have the appropriate knowledge or expertise to contribute to this type of decision-making.

Source: The Scottish Government. (2013). Public Acceptability of Data Sharing Between the Public, Private and Third Sectors for Research Purposes. In The Scottish Government. https://www.google.com/url?q=https://www.gov.scot/publications/public-acceptability-data-sharing-between-public-private-third-sectors-research-purposes/&sa=D&source=docs&ust=1683813247138360&usg=AOvVaw345uiofE3iELOHd6LbG5Sz

USA

The Data Assembly in New York

Description: 

The Data Assembly began in the summer of 2020 with an initial focus on the response to the COVID-19 pandemic in New York City. Remote deliberations with three “mini-publics” composed of data holders, policymakers, representatives of civic rights, advocacy organisations, and citizens were held to create recommendations to guide the data-driven response to COVID-19 and other emerging threats.

Results: 

The recommendations from the discussions are the following

●      Match urgency with accountability: participants agreed that in case of a health emergency, they would accept increased surveillance but the organisations should comply with mechanisms that guarantee public accountability. 

●      Support and expand data literacy: clear communication is important for different stakeholders to understand the benefits of data reuse. 

●      Centre equity: To avoid reproducing existing inequalities, organisations should assess if their data intended to be reused misrepresent any population or can cause any harm. 

●      Engage legitimate, local actors: participants highlighted the need to include local actors, especially organisations working at the local level 

●      Develop positions for responsible data reuse: there is a need for data stewards that can help organisations manage their data and coordinate the interactions with the different stakeholders in the data sphere. 

Source: Young, A., Verhulst, S. G., Safonova, N., & Zahuranec, A. J. (2020). Responsible Data Re-Use Framework. In The Data Assembly. https://thedataassembly.org/files/nyc-data-assembly-report.pdf

UK

The ethical, legal, and social implications of data governance

The ethical, legal, and social implications of data governance during pandemics in the UK

Description: 

Two-week-long online citizens’ juries were organised to identify best practices for ensuring transparent, accountable, and inclusive data governance in the UK. A total of 50 citizens were recruited as jurors representing the demographic diversity of the UK population according to age, gender, ethnicity, and region. The jurors discussed data-driven technologies that played roles in the UK’s response to COVID-19, including vaccine passports, risk-scoring algorithms, and the GDPR. They were tasked with addressing the following questions: 

1. What constitutes good governance of data-driven technologies?  
2. What constitutes proportionate uses of data-driven technologies during pandemics? 

Results: 

These are the main conclusions from the jurors:

1. Transparency, communication, and clarity: there must be clear and consistent communication around the use of data-driven approaches, and the application of rules and public health measures during a pandemic.
2. Accountability: there must be an emphasis on adherence to the rule of law, protecting democracy, and ensuring robust, fair and equal enforcement of policies.
3. Equity, inclusiveness, and non-discrimination: the use of data-driven technologies should not exacerbate unequal social stratification.
4. Protection of personal freedoms: use of data-driven technologies should respect and protect individual liberties and rights.
5. Proportionate and time-limited uses: data use must balance public health needs and risks to individuals and society, and pandemic response measures must not extend into post-pandemic data futures.
6. Emergency preparedness and planning: effective, accurate, and responsibly managed data should be the basis for evidence and learning during emergency preparedness and planning.
7. Trustworthiness: the organisations and governance structures involved in the design and use of a data-driven technology must be trustworthy.

Source: Ada Lovelace Institute. (2022, July). The rule of trust. Www.adalovelaceinstitute.org. https://www.adalovelaceinstitute.org/report/trust-data-governance-pandemics/

UK

The use of data and statistics for “Public Good” in the UK

Description:

Administrative Data Research UK (ADR UK) and the Office for Statistics Regulation (OSR) partnered to explore public perceptions and understanding around the concept of what ‘public good’ means with regard to the use of data and statistics. These two organisations collaborated by developing a UK-wide public dialogue using workshops. Their overall aim was to develop a resource answering their primary question of “What are public perceptions around ‘public good’ use of data and statistics?’. Throughout these workshops, they explored the following sub-questions. 

1. How should ‘public good’ be defined and/or measured when making decisions about sharing data for research?
2. What uses of data and statistics are considered to be in the ‘public good?’
3. Are some uses of data and statistics ‘more’ in the public good than others? 
4. Are there conceptual differences between the phrases ‘public good and public interest’, public benefit, public welfare, common good, greater good, societal benefit, or other similar phrases (which are sometimes used interchangeably in the literature)?

Deliberative discussions were chosen as the method of engagement with 68 participants who lived in the UK. Four in-person workshops were held across London, Cardiff, Glasgow, and Belfast, and one workshop was held online for participants who were unable to attend in person. Additionally, an Advisory Board was created with individuals with relevant expertise to ensure important stakeholders were involved and appropriate dialogue was conducted.

Results: 

Here are the main recommendations identified by the participants:

1. Public involvement: citizens want to be involved and informed on how data in research and statistics are being used to serve the public good. Participants suggested that inclusive panels and public conversations should be held for the decision-making about data and statistics.
2. Real-world needs: citizens agree that research and statistics should aim to address the most pressing world needs, especially social inequity and social inequality. Participants recommended that the public have access to the decision-making process of Data Access Committees to understand the impact of proposed projects. 
3. Clear communication: participants recognize the importance of proactive communication that is clear and accessible that creates awareness of the importance, the motivations, and the outcomes of public good use of data for research and statistics
4. Minimize harm: data should not contribute to anything harmful, especially its use should avoid perpetuating stereotypes of certain groups of people. The public suggested consulting citizens with lived experience about potential uses of data or the interpretation of statistical patterns. Moreover, the participants agreed on the importance of increasing accountability from the experts working with data and statistics. 
5. Best practice safeguarding: citizens identified the importance of a framework, such as the Five Safes, that can be universally used to feel confident that public sector data is being used in a way they can trust. 

Source: Harkness , F., Rijneveld, C., Liu, Y., Kashef, S., & Cowan, M. (2022). A UK-wide public dialogue exploring what the public perceive as “public good” use of data for research and statistics. https://www.adruk.org/fileadmin/uploads/adruk/Documents/PE_reports_and_documents/ADR_UK_OSR_Public_Dialogue_final_report_October_2022.pdf

Germany

Citizen Engagement and Innovative Data Use for Africa’s Development (DataCipation)

Description: 

The programme supports AU Organs in their quest to intensify citizen engagement and the role of data, digital and non-digital approaches in their programmes and initiatives. The programme is implemented in cooperation with the African Union Commission and the AU Development Agency (AUDA-NEPAD). The political partner for the programme is the Bureau of the Chairperson at the African Union Commission, demonstrating the high political commitment of the AU to the programme.

The programme takes a systemic approach, focusing on implementation across three main areas as follows:

1. Connecting policymakers with Africa’s data and digital innovators for good governance and development by enhancing the collaboration and cooperation of the AU Organs and Member States with Africa’s digital innovation ecosystem.
2. Improving citizen participation in good governance and development through innovative communications and engagement methodologies; leveraging data and digital and non-digital approaches.
3. Supporting the implementation of digital policies across Africa to improve access to meaningful participation of citizens in the digital transformation and to exploit the related potentials for social and economic development

Success factors included, enabling the African Union Commission to lead by example for digital transformation in public sector innovation as well as interactive, participatory communications efforts.Secondly it allowed for the building coalitions of the willing of AU Member States for spearheading novel digital policy approaches that pave the way for broader adoption at continental level. And lastly it permitted for a trusted partner to AUC and African policymakers by providing independent expertise geared towards realising the strategic interests including techno-geopolitical sovereignty of the African continent. 

Source: giz. (2021, December). Citizen Engagement and Innovative Data Use for Africa’s Development (DataCipation). Www.giz.de. https://www.giz.de/en/worldwide/98533.html

USA

Equitable Data Engagement and Accountability

Description: 

In its final report in April 2022— Vision for Equitable Data —the Equitable Data Working Group emphasised the need for the Federal government to use equitable data to:

1. Encourage diverse collaborations across levels of government, civil society, and the research community
2. Be accountable to the American public. By equitable data, we mean data that allow for rigorous assessment of the extent to which government programs and policies yield consistently fair, just, and impartial treatment of all individuals, including those who have been historically underserved, marginalized, and adversely affected by persistent poverty and inequality.

Fair data can bring attention to opportunities for targeted actions that will lead to noticeable improved results for marginalized communities. On essential characteristic of fair data is its disaggregation by demographic factors ( e.g., race, ethnicity, gender, language spoken, etc.), geographic factors ( e.g., rural/urban), or other variables, which allow for insights to disparities in access to and outcomes from government programs, policies and services

Establishing a robust and fair data infrastructure requires developing collaborations among various levels of government, as well as with a diverse array of external organizations, in order to advance outcomes for underserved communities. Building such infrastructure will likely need new incentives and avenues, including promoting greater data sharing and capacity building across different levels of government and expanding the research community engaged in producing and analyzing fair data. 

Moreover, it is essential to provide tools that enable civil society organizations and communities to utilize and visualize federal data and track the government’s progress towards achieving fair outcomes. This is crucial for enhancing accountability and trustworthiness with the American public.These tools should encourage community involvement in government equity initiatives, but they must be designed and implemented in a manner that aligns with the data analysis skills and resources available to community members. Ideally, these tools should allow the public to easily access meaningful and actionable data about the well-being of their communities and services provided to them. 

Source: Office of Science and Technology Policy. (2022). Request for Information; Equitable Data Engagement and Accountability (107th ed., Vol. 87, pp. 54269–54270). Federal Register. https://www.govinfo.gov/content/pkg/FR-2022-09-02/pdf/2022-19007.pdf

UK, USA, Singapore

How digital transformation can be made good for all

Description:

The aim of this project investigated the associated risks in locating digital transformation strategies outside communities, as well as their preferences and priorities when moving into a digital environment. Specifically, the project theories that top-down approaches to digital transformation and digital readiness can overlook important demographic differences in communities in areas of digital literacy, digital familiarity, access to technology, and consent. 

The project initially looks at case studies in the UK and Singapore and the characteristics of vulnerable recipients in digital transformation, critiquing top-down approaches. Secondly, it examines current examples of ‘co-creation policies’ that can be found in the Chicago police community. This allows for the identification of understanding effective methods for citizen participation and obstacles to bottom-up policy development when disabled or disadvantaged communities are included. Lastly, the concept of Living Digital Transformation (LDT) is introduced and examined in terms of how bottom-up and user-centric digitising is a more sustainable and inclusive approach, in the context of university communities. 

Source: Findlay, M., & Shanmugam, S. (2023). Participatory Digital Futures: How digital transformation can be made good for all. SSRN, 2. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430120

UK

Belfast to launch “Citizen Office of Digital Innovation”

Description:

In 2022, Belfast, located in Northern Ireland launched and piloted a program called Citizen Office of Digital Innovation (CODI). It aimed to increase resident engagement when it comes to data and technology. Overall, the program looks to support ‘digital citizenship skills’ which are part of the Smart Belfast Programme. This looks to help citizens to develop a better understanding of how technology is utilized in Belfast. Within the program, creative and interactive methods of engagement were used to explore concepts like co-design, citizen science, the Internet of Things, Artificial Intelligence, data, science, and privacy.  

Source: Wray, S. (Ed.). (2022, September 1). Belfast to launch “Citizen Office of Digital Innovation.” Cities Today. https://www.itu.int/hub/2022/09/belfast-to-launch-citizen-office-of-digital-innovation/

UK

Explainable AI in the UK

Description: 

Two juries were selected, one in Coventry and one in Manchester, with the purpose of analysing the importance the public gives to receiving an explanation when AI was used in their healthcare, focusing on the tradeoff between the accuracy and explainability of AI systems. The jurors were made up of a cross-section of the population, representing the demographic breakdown of England as per the 2011 Census. In total, 36 individuals were selected to be jurors. They considered the tradeoff accuracy-explainability in four different scenarios: 

1. Healthcare: diagnosis of acute stroke 
2. Healthcare: finding matches between kidney transplant donors and recipients 
3. Criminal Justice: deciding which offenders should be referred to a rehabilitation programme 
4. Recruitment: screening job applications and making shortlisting decisions

Results: 

Three key themes relating to explaining AI decisions emerged from the research:

1. The importance of context for the relevance of the explanation required
2. The need for education and awareness on the use of AI for decision-making
3. The challenges in providing explanations of AI at the expense of less accurate decision-making

Most jurors felt that the relative importance of explanations and accuracy varied by context. In contexts where humans would usually provide an explanation, most jurors indicated that explanations of AI decisions should be similar to human explanations. Jurors felt this was important to help build trust and to ensure explanations were understandable.

Source: Information Commissioner’s Office. (2019). Project ExplAIn Interim Report. https://ico.org.uk/media/2615039/project-explain-20190603.pdf

UK

Care.data

Description: 

The program care.data was made public in 2013 by the Health and Social Care Information Centre. The goal was to use data collected by GP surgeries and store them in a central database using the General Practice Extraction Service (GPES). Those who were part of GP practices were instructed that their health information would be stored in the database. Members of the GP practices who did not want their information to be uploaded were allowed to object. 

In this database, patients’ data was anonymized and could be accessed by health care researchers, managers and planners within the NHS, as well as academic institutions and organizations. The software and services used for this program were provided by Atos. 

The care.data program was heavily criticized and was deemed controversial since it was launched due to many overlooked factors. It was first critiqued around the lack of patient awareness regarding the program as well as lacking obvious options for objecting in the leaflet sent to homes. Additionally, the leaflet only described the program and the benefits surrounding it but did not include the opt-out form. 

After multiple reviews, the care.data program ended due to “major issues with project definition, schedule, budget, quality and/or benefits delivery, which at this stage do not appear to be manageable or resolvable” (Ramesh, 2015). Moreover, in December 2015 Atos was criticized by the Public Accounts Committee and was accused of taking advantage of the Department of Health. After a few years, the program ended in July 2016. 

The authors of The social license for research: why care.data ran into trouble argue that despite obtaining a lawful infrastructure for the implementation of the program, no social license was secured which led to challenges. 

Care.data could have been successful if three areas were recognized:

1. Establishing trust and confidence in the governance of research needs to go beyond focusing on economic gains and also consider the patient’s concerns as an individual seeking care. Meaning, their concerns as a citizen, who is part of a larger social fabric, is different to the concerns as an individual patient. 
2. In order for initiatives like care.data to be successful, patients must have confidence that their medical records will be securely and appropriately managed, with consideration of anonymization and public interest. 
3. Respecting the conditions of the social license involves upholding principles such as reciprocity, avoiding exploitation and prioritizing the public good.  

Source: Carter, P., Laurie, G. T., & Dixon-Woods, M. (2015). The social licence for research: whycare.dataran into trouble. Journal of Medical Ethics, 41(5), 404–409. https://doi.org/10.1136/medethics-2014-102374

Additional Links: 

●      Ramesh, R. (2015, June 26). NHS patient data plans unachievable, review finds. The Guardian. https://www.theguardian.com/politics/2015/jun/26/nhs-patient-data-plans-unachievable-review-health

●      Wikipedia . (2020, July 31). Care.data. Wikipedia. https://en.wikipedia.org/wiki/Care.data

UK

Public Acceptability of Data Sharing

Public Acceptability of Data Sharing Between the Public, Private, and Third Sectors for Research Purposes

Description: 

In 2012 the Scottish Government commissioned research to explore the public acceptability of cross-sectoral data linkage for research and statistical purposes to inform the ongoing development of a Scotland-wide Data Linkage Framework. The research indicated that the public was, in principle, broadly supportive of data linkage, particularly for health research, and of the overall objectives of the Data Linkage Framework. However, this support was conditional and a range of ambivalences and concerns were also expressed: there was significant unease about the private sector having access to public sector data and, more specifically, about the scope for commercial gain arising from data linkage. 

Report of a deliberative research project on the public’s attitudes toward data sharing. It focuses particularly on a) the public’s opinion about data sharing with the private and third sector; b) the acceptability of different methods for sharing benefits gained from the use of their data; and c) the appeal of different methods for empowering citizens in decision making about the use of their data. The study was conducted using a combination of primary and secondary research methods, comprising:

●      a desk-based literature review of international benefit-sharing models arising from the value of data sharing

●      a desk-based literature review of different methods that have been used to empower citizens in decision-making about how their data are used

●      a series of deliberative events with members of the public

Results: 

In the results, there is a dedicated section called ‘Empowering Citizens in Decision Making’. Within this section, the researchers determined the following: 

1. Participants in the discussion on public involvement in decision making regarding data sharing unanimously agreed that it was important and appropriate for the public to be involved in deciding how their data is used. 
2. When given 5 broad forms of public involvement: transparency, feedback, agenda-setting, informing policy, and representation, the most preferred formats decided by participants were transparency, feedback and informing policy.

 .      Transparency: participants wanted to know how their data are used and shared, including a rationale for why it is being shared, what type of data is being shared, how sharing works in practice and who will be able to access the data. 

 .      Feedback: this was important for informing the public about how research carried out using their data has benefited society. 

 .      Public involvement in policy-making: would allow the public, to a degree, have some control over how their data is used.

 .      Agenda-setting and representation: these were the least preferred methods as participants felt as if the public may know have the appropriate knowledge or expertise to contribute to this type of decision-making.

Source: The Scottish Government. (2013). Public Acceptability of Data Sharing Between the Public, Private and Third Sectors for Research Purposes. In The Scottish Government. https://www.google.com/url?q=https://www.gov.scot/publications/public-acceptability-data-sharing-between-public-private-third-sectors-research-purposes/&sa=D&source=docs&ust=1683813247138360&usg=AOvVaw345uiofE3iELOHd6LbG5Sz

Germany

Citizen Engagement and Innovative Data Use for Africa’s Development (DataCipation)

Description: 

The programme supports AU Organs in their quest to intensify citizen engagement and the role of data, digital and non-digital approaches in their programmes and initiatives. The programme is implemented in cooperation with the African Union Commission and the AU Development Agency (AUDA-NEPAD). The political partner for the programme is the Bureau of the Chairperson at the African Union Commission, demonstrating the high political commitment of the AU to the programme.

The programme takes a systemic approach, focusing on implementation across three main areas as follows:

1. Connecting policymakers with Africa’s data and digital innovators for good governance and development by enhancing the collaboration and cooperation of the AU Organs and Member States with Africa’s digital innovation ecosystem.
2. Improving citizen participation in good governance and development through innovative communications and engagement methodologies; leveraging data and digital and non-digital approaches.
3. Supporting the implementation of digital policies across Africa to improve access to meaningful participation of citizens in the digital transformation and to exploit the related potentials for social and economic development

Success factors included, enabling the African Union Commission to lead by example for digital transformation in public sector innovation as well as interactive, participatory communications efforts.Secondly it allowed for the building coalitions of the willing of AU Member States for spearheading novel digital policy approaches that pave the way for broader adoption at continental level. And lastly it permitted for a trusted partner to AUC and African policymakers by providing independent expertise geared towards realising the strategic interests including techno-geopolitical sovereignty of the African continent. 

Source: giz. (2021, December). Citizen Engagement and Innovative Data Use for Africa’s Development (DataCipation). Www.giz.de. https://www.giz.de/en/worldwide/98533.html

USA

Keating Memorial Self-Research

Description

The Open Humans Foundation is a nonprofit organisation dedicated to empowering individuals and communities around their personal data, to explore and share for the purposes of education, health, and research. The organisation operates and manages Open Humans, a project and community that enables individuals to connect their data with research and citizen science.

The Keating Memorial Self-Research encourages people to share their ideas for self-research projects with the community so different people can work on the project and share their data to contribute to the project´s analysis. The project uses a participatory research methodology, specifically a citizen science approach, to engage individuals in the collection, sharing, and analysis of personal health data. This approach involves the voluntary contribution of personal data by individuals to research studies, providing tools and resources for data management and analysis, and collaboration with researchers and other participants.

The mechanisms to engage the people in the different projects are through a monthly community call, focused on discussing topics of interest, management tools and potential collaborations, and weekly self-research chats, to discuss the self-research projects, progress, and insights. 

Through Open Humans, individuals can gain control over their personal health data, and participate in research that is aligned with their values and needs. The project emphasizes community engagement, transparency, and empowerment, and seeks to promote more inclusive and collaborative models of research.

Results: 

184 people joined the Keating Memorial project but no activity has been registered since 2021. However, the Open Humans platform has more than 30 ongoing projects related to different topics like genome, urban mobility, and different types of diseases- where people can share and explore their data. 

Source: Open Humans. (n.d.). About – Open Humans. Www.openhumans.org. Retrieved May 11, 2023, from https://www.openhumans.org/about/

UK

Belfast to launch “Citizen Office of Digital Innovation”

Description:

In 2022, Belfast, located in Northern Ireland launched and piloted a program called Citizen Office of Digital Innovation (CODI). It aimed to increase resident engagement when it comes to data and technology. Overall, the program looks to support ‘digital citizenship skills’ which are part of the Smart Belfast Programme. This looks to help citizens to develop a better understanding of how technology is utilized in Belfast. Within the program, creative and interactive methods of engagement were used to explore concepts like co-design, citizen science, the Internet of Things, Artificial Intelligence, data, science, and privacy.  

Source: Wray, S. (Ed.). (2022, September 1). Belfast to launch “Citizen Office of Digital Innovation.” Cities Today. https://www.itu.int/hub/2022/09/belfast-to-launch-citizen-office-of-digital-innovation/

UK

The use of data and statistics for “Public Good” in the UK

Description:

Administrative Data Research UK (ADR UK) and the Office for Statistics Regulation (OSR) partnered to explore public perceptions and understanding around the concept of what ‘public good’ means with regard to the use of data and statistics. These two organisations collaborated by developing a UK-wide public dialogue using workshops. Their overall aim was to develop a resource answering their primary question of “What are public perceptions around ‘public good’ use of data and statistics?’. Throughout these workshops, they explored the following sub-questions. 

1. How should ‘public good’ be defined and/or measured when making decisions about sharing data for research?
2. What uses of data and statistics are considered to be in the ‘public good?’
3. Are some uses of data and statistics ‘more’ in the public good than others? 
4. Are there conceptual differences between the phrases ‘public good and public interest’, public benefit, public welfare, common good, greater good, societal benefit, or other similar phrases (which are sometimes used interchangeably in the literature)?

Deliberative discussions were chosen as the method of engagement with 68 participants who lived in the UK. Four in-person workshops were held across London, Cardiff, Glasgow, and Belfast, and one workshop was held online for participants who were unable to attend in person. Additionally, an Advisory Board was created with individuals with relevant expertise to ensure important stakeholders were involved and appropriate dialogue was conducted.

Results: 

Here are the main recommendations identified by the participants:

1. Public involvement: citizens want to be involved and informed on how data in research and statistics are being used to serve the public good. Participants suggested that inclusive panels and public conversations should be held for the decision-making about data and statistics.
2. Real-world needs: citizens agree that research and statistics should aim to address the most pressing world needs, especially social inequity and social inequality. Participants recommended that the public have access to the decision-making process of Data Access Committees to understand the impact of proposed projects. 
3. Clear communication: participants recognize the importance of proactive communication that is clear and accessible that creates awareness of the importance, the motivations, and the outcomes of public good use of data for research and statistics
4. Minimize harm: data should not contribute to anything harmful, especially its use should avoid perpetuating stereotypes of certain groups of people. The public suggested consulting citizens with lived experience about potential uses of data or the interpretation of statistical patterns. Moreover, the participants agreed on the importance of increasing accountability from the experts working with data and statistics. 
5. Best practice safeguarding: citizens identified the importance of a framework, such as the Five Safes, that can be universally used to feel confident that public sector data is being used in a way they can trust. 

Source: Harkness , F., Rijneveld, C., Liu, Y., Kashef, S., & Cowan, M. (2022). A UK-wide public dialogue exploring what the public perceive as “public good” use of data for research and statistics. https://www.adruk.org/fileadmin/uploads/adruk/Documents/PE_reports_and_documents/ADR_UK_OSR_Public_Dialogue_final_report_October_2022.pdf

UK

Explainable AI in the UK

Description: 

Two juries were selected, one in Coventry and one in Manchester, with the purpose of analysing the importance the public gives to receiving an explanation when AI was used in their healthcare, focusing on the tradeoff between the accuracy and explainability of AI systems. The jurors were made up of a cross-section of the population, representing the demographic breakdown of England as per the 2011 Census. In total, 36 individuals were selected to be jurors. They considered the tradeoff accuracy-explainability in four different scenarios: 

1. Healthcare: diagnosis of acute stroke 
2. Healthcare: finding matches between kidney transplant donors and recipients 
3. Criminal Justice: deciding which offenders should be referred to a rehabilitation programme 
4. Recruitment: screening job applications and making shortlisting decisions

Results: 

Three key themes relating to explaining AI decisions emerged from the research:

1. The importance of context for the relevance of the explanation required
2. The need for education and awareness on the use of AI for decision-making
3. The challenges in providing explanations of AI at the expense of less accurate decision-making

Most jurors felt that the relative importance of explanations and accuracy varied by context. In contexts where humans would usually provide an explanation, most jurors indicated that explanations of AI decisions should be similar to human explanations. Jurors felt this was important to help build trust and to ensure explanations were understandable.

Source: Information Commissioner’s Office. (2019). Project ExplAIn Interim Report. https://ico.org.uk/media/2615039/project-explain-20190603.pdf

UK

The use of data and statistics for “Public Good” in the UK

Description:

Administrative Data Research UK (ADR UK) and the Office for Statistics Regulation (OSR) partnered to explore public perceptions and understanding around the concept of what ‘public good’ means with regard to the use of data and statistics. These two organisations collaborated by developing a UK-wide public dialogue using workshops. Their overall aim was to develop a resource answering their primary question of “What are public perceptions around ‘public good’ use of data and statistics?’. Throughout these workshops, they explored the following sub-questions. 

1. How should ‘public good’ be defined and/or measured when making decisions about sharing data for research?
2. What uses of data and statistics are considered to be in the ‘public good?’
3. Are some uses of data and statistics ‘more’ in the public good than others? 
4. Are there conceptual differences between the phrases ‘public good and public interest’, public benefit, public welfare, common good, greater good, societal benefit, or other similar phrases (which are sometimes used interchangeably in the literature)?

Deliberative discussions were chosen as the method of engagement with 68 participants who lived in the UK. Four in-person workshops were held across London, Cardiff, Glasgow, and Belfast, and one workshop was held online for participants who were unable to attend in person. Additionally, an Advisory Board was created with individuals with relevant expertise to ensure important stakeholders were involved and appropriate dialogue was conducted.

Results: 

Here are the main recommendations identified by the participants:

1. Public involvement: citizens want to be involved and informed on how data in research and statistics are being used to serve the public good. Participants suggested that inclusive panels and public conversations should be held for the decision-making about data and statistics.
2. Real-world needs: citizens agree that research and statistics should aim to address the most pressing world needs, especially social inequity and social inequality. Participants recommended that the public have access to the decision-making process of Data Access Committees to understand the impact of proposed projects. 
3. Clear communication: participants recognize the importance of proactive communication that is clear and accessible that creates awareness of the importance, the motivations, and the outcomes of public good use of data for research and statistics
4. Minimize harm: data should not contribute to anything harmful, especially its use should avoid perpetuating stereotypes of certain groups of people. The public suggested consulting citizens with lived experience about potential uses of data or the interpretation of statistical patterns. Moreover, the participants agreed on the importance of increasing accountability from the experts working with data and statistics. 
5. Best practice safeguarding: citizens identified the importance of a framework, such as the Five Safes, that can be universally used to feel confident that public sector data is being used in a way they can trust. 

Source: Harkness , F., Rijneveld, C., Liu, Y., Kashef, S., & Cowan, M. (2022). A UK-wide public dialogue exploring what the public perceive as “public good” use of data for research and statistics. https://www.adruk.org/fileadmin/uploads/adruk/Documents/PE_reports_and_documents/ADR_UK_OSR_Public_Dialogue_final_report_October_2022.pdf

UK

Control Access to Patient Records for Research in the UK

Description: 

The secondary use of health data for research raises complex questions of privacy and governance. Such questions are ill-suited to opinion polling where citizens must choose quickly between multiple-choice answers based on little information. Two citizens´ juries, of 17 citizens each, were convened for three days to reflect on what control informed citizens would seek over the use of health records for research. Each jury answered, “To what extent should patients control access to patient records for secondary use?”. Jurors heard from and questioned five expert witnesses. Their individual views were polled using questionnaires at the beginning and at the end of the process.

Results: 

33 out of 34 jurors voted in support of the secondary use of data for research, with 24 wanting individuals to be able to opt out, six favoring opt-in, and three voting that all records should be available without any consent process. When considering who should get access to data, both juries had very similar rationales. Both thought that public benefit was a key justification for access. Jury 1 was more strongly supportive of sharing patient records for public benefit, in contrast, jury 2 was more cautious and sought to give patients more control.

The findings show that, when informed of both risks and opportunities associated with data sharing, citizens believe an individual’s right to privacy should not prevent research that can benefit the general public. 

Source: Tully, M. P., Bozentko, K., Clement, S., Hunn, A., Hassan, L., Norris, R., Oswald, M., & Peek, N. (2018). Investigating the Extent to Which Patients Should Control Access to Patient Records for Research: A Deliberative Process Using Citizens’ Juries (2nd ed., Vol. 20). Journal of Medical Internet Research. https://www.jmir.org/2018/3/e112

UK

Care.data

Description: 

The program care.data was made public in 2013 by the Health and Social Care Information Centre. The goal was to use data collected by GP surgeries and store them in a central database using the General Practice Extraction Service (GPES). Those who were part of GP practices were instructed that their health information would be stored in the database. Members of the GP practices who did not want their information to be uploaded were allowed to object. 

In this database, patients’ data was anonymized and could be accessed by health care researchers, managers and planners within the NHS, as well as academic institutions and organizations. The software and services used for this program were provided by Atos. 

The care.data program was heavily criticized and was deemed controversial since it was launched due to many overlooked factors. It was first critiqued around the lack of patient awareness regarding the program as well as lacking obvious options for objecting in the leaflet sent to homes. Additionally, the leaflet only described the program and the benefits surrounding it but did not include the opt-out form. 

After multiple reviews, the care.data program ended due to “major issues with project definition, schedule, budget, quality and/or benefits delivery, which at this stage do not appear to be manageable or resolvable” (Ramesh, 2015). Moreover, in December 2015 Atos was criticized by the Public Accounts Committee and was accused of taking advantage of the Department of Health. After a few years, the program ended in July 2016. 

The authors of The social license for research: why care.data ran into trouble argue that despite obtaining a lawful infrastructure for the implementation of the program, no social license was secured which led to challenges. 

Care.data could have been successful if three areas were recognized:

1. Establishing trust and confidence in the governance of research needs to go beyond focusing on economic gains and also consider the patient’s concerns as an individual seeking care. Meaning, their concerns as a citizen, who is part of a larger social fabric, is different to the concerns as an individual patient. 
2. In order for initiatives like care.data to be successful, patients must have confidence that their medical records will be securely and appropriately managed, with consideration of anonymization and public interest. 
3. Respecting the conditions of the social license involves upholding principles such as reciprocity, avoiding exploitation and prioritizing the public good.  

Source: Carter, P., Laurie, G. T., & Dixon-Woods, M. (2015). The social licence for research: whycare.dataran into trouble. Journal of Medical Ethics, 41(5), 404–409. https://doi.org/10.1136/medethics-2014-102374

Additional Links: 

●      Ramesh, R. (2015, June 26). NHS patient data plans unachievable, review finds. The Guardian. https://www.theguardian.com/politics/2015/jun/26/nhs-patient-data-plans-unachievable-review-health

●      Wikipedia . (2020, July 31). Care.data. Wikipedia. https://en.wikipedia.org/wiki/Care.data

UK

Fair uses of NHS patients´ data and NHS operational data

Description:

A public engagement program was held to understand what people think about the NHS allowing third parties to access their health data, ranging from academics, charities, or industries. The analysis centred on the system of rules and environment that would make a health system trustworthy. 

 The mixed methods used were:

1. Three round tables involving patient representatives in Oxford, Manchester, and London: their aim was to design the stimulus materials and the method of testing the charge question for the Citizen’s Jury. There were a total of 30 patient representatives. 
2. Citizen’s Juries in Taunton, Leeds, and London:  to discuss the question, ‘What constitutes a fair partnership between the NHS and researchers, charities and industry on uses of NHS patients’ data and NHS operational data?’. This involved 60 jurors over two and a half days.
3. A nationally representative survey from the UK: 2095 people completed the survey. It quantitatively explored important topics jurors focused on and tested broader public opinion on several key themes that emerged including the level of awareness of data access partnerships in a representative sample and aspects of communication raised by jurors. 

Results:

Specifically looking at the results around citizens’ involvement the jurors decided that citizens need to be more involved at different decision-making stages, especially in policy and practices. Meaning, citizens should be present and encouraged to participate in the process of establishing and managing data access partnerships. The jurors identified three engagement management processes that can be used to include citizens: 

1. Citizens´ Juries and deliberation for key decisions
2. Public votes to approve local partnerships
3. Playing a role in governance boards

Moreover, jurors proposed that each data access partnership should publish reports and case studies to provide transparency to the public. Moreover, jurors concluded that communities and individuals would be more resistant to data access partnerships using their data if procedures and methodologies are unclear. To increase trust, it is essential to create an awareness campaign about the national data opt-out service to increase trust and confidence in the system. 

Source: Hopkins, H., Kinsella, S., Van, A., Hopkins, M., & Mil, V. (2020). Foundations of fairness: views on uses of NHS patients’ data and NHS operational data A mixed methods public engagement programme with integrated Citizens’ Juries Findings Report. https://understandingpatientdata.org.uk/sites/default/files/2020-03/Foundations%20of%20Fairness%20-%20Full%20Research%20Report.pdf

USA

The Data Assembly in New York

Description: 

The Data Assembly began in the summer of 2020 with an initial focus on the response to the COVID-19 pandemic in New York City. Remote deliberations with three “mini-publics” composed of data holders, policymakers, representatives of civic rights, advocacy organisations, and citizens were held to create recommendations to guide the data-driven response to COVID-19 and other emerging threats.

Results: 

The recommendations from the discussions are the following

●      Match urgency with accountability: participants agreed that in case of a health emergency, they would accept increased surveillance but the organisations should comply with mechanisms that guarantee public accountability. 

●      Support and expand data literacy: clear communication is important for different stakeholders to understand the benefits of data reuse. 

●      Centre equity: To avoid reproducing existing inequalities, organisations should assess if their data intended to be reused misrepresent any population or can cause any harm. 

●      Engage legitimate, local actors: participants highlighted the need to include local actors, especially organisations working at the local level 

●      Develop positions for responsible data reuse: there is a need for data stewards that can help organisations manage their data and coordinate the interactions with the different stakeholders in the data sphere. 

Source: Young, A., Verhulst, S. G., Safonova, N., & Zahuranec, A. J. (2020). Responsible Data Re-Use Framework. In The Data Assembly. https://thedataassembly.org/files/nyc-data-assembly-report.pdf

UK

The ethical, legal, and social implications of data governance

The ethical, legal, and social implications of data governance during pandemics in the UK

Description: 

Two-week-long online citizens’ juries were organised to identify best practices for ensuring transparent, accountable, and inclusive data governance in the UK. A total of 50 citizens were recruited as jurors representing the demographic diversity of the UK population according to age, gender, ethnicity, and region. The jurors discussed data-driven technologies that played roles in the UK’s response to COVID-19, including vaccine passports, risk-scoring algorithms, and the GDPR. They were tasked with addressing the following questions: 

1. What constitutes good governance of data-driven technologies?  
2. What constitutes proportionate uses of data-driven technologies during pandemics? 

Results: 

These are the main conclusions from the jurors:

1. Transparency, communication, and clarity: there must be clear and consistent communication around the use of data-driven approaches, and the application of rules and public health measures during a pandemic.
2. Accountability: there must be an emphasis on adherence to the rule of law, protecting democracy, and ensuring robust, fair and equal enforcement of policies.
3. Equity, inclusiveness, and non-discrimination: the use of data-driven technologies should not exacerbate unequal social stratification.
4. Protection of personal freedoms: use of data-driven technologies should respect and protect individual liberties and rights.
5. Proportionate and time-limited uses: data use must balance public health needs and risks to individuals and society, and pandemic response measures must not extend into post-pandemic data futures.
6. Emergency preparedness and planning: effective, accurate, and responsibly managed data should be the basis for evidence and learning during emergency preparedness and planning.
7. Trustworthiness: the organisations and governance structures involved in the design and use of a data-driven technology must be trustworthy.

Source: Ada Lovelace Institute. (2022, July). The rule of trust. Www.adalovelaceinstitute.org. https://www.adalovelaceinstitute.org/report/trust-data-governance-pandemics/

UK, India, South Africa

Mindkind: Global mental health databank pilot

Description: 

Over the course of two years, Wellcome trust developed a pilot project which aimed at exploring the governance of data related to mental health. They joined with Sage Bionetworks to create a prototype and test the ability to build a global mental health databank (GMHD). This databank included longitudinal and electronically-derived data from youth, with an emphasis on the approaches, treatments, and interventions potentially relevant to anxiety or depression. To develop the project, a participatory data governance process was implemented involving youth aged between 14-24 in the decision-making process of the design, collection, sharing, analysis, reuse, and use of data by the researchers. The following participatory mechanisms were created: 

1. Young People’s Advisory Group: Each country had a Young People’s Advisory Group that met regularly online to discuss the study design and data collection. In total there were 32 participants from a target group of young people aged 16-24 with lived experience of mental health challenges.
2. Professional Youth Advisor: Each country recruited a full-time youth advisor as a member of the study team. Their role was to act as a link between the Young People’s Advisory Group and the project teams and support research directions. In total there were 3 professional youth advisors.
3. Global Youth Panel: The panel was composed by members with past experience on youth panels and advocacy groups for mental health issues. They met monthly to provide high-level feedback on project decisions. There were 15 participants in the panel.
4. International Youth Panel: Due to delays in the recruitment of young people for the Young People’s Advisory group, this panel was created ad hoc. The objective of this panel was to identify “active ingredients” of mental health, map data governance models and data collection strategies. 
5. Data Use Advisory Group: This group was responsible for providing scientific insights on the use of a global mental health databank and discussing its ethical issues. The members were from 7 countries (Australia, Brazil, India, Nigeria, South Africa, United Kingdom, and USA). There were 18 participants in the group.
6. Randomised Control Trial: A RCT was designed with participants from India, South Africa and the UK to test the governance conditions of the MindKind app after recording information about their mental health and behaviours. The sample was of 300 participants from a target group of young people that at least 10% had experienced mental health challenges.
7. Deliberative Democracy Sessions: Two rounds of deliberative democracy sessions were held in each country. There were in total 150 participants discussing different data governance models, benefits and concerns around data governance for a global mental health databank. 

Results: There was no statistically significant difference in enrollment rates across data governance models, even though the study expected that youngsters will prefer governance models that provide them more control over their data, they enrolled in the program regardless of the control over how their data was used or accessed. Moreover, participants given a choice of study topics showed a statistically significantly lower engagement with the study than participants who were assigned fixed study topics.

Source: 

Connected by Data. (n.d.). Connected by data | MindKind: Global mental health databank pilot. Connectedbydata.org. Retrieved May 11, 2023, from http://connectedbydata.org/cases/mindkind-global-mental-health-databank-pilot

USA

Equitable Data Engagement and Accountability

Description: 

In its final report in April 2022— Vision for Equitable Data —the Equitable Data Working Group emphasised the need for the Federal government to use equitable data to:

1. Encourage diverse collaborations across levels of government, civil society, and the research community
2. Be accountable to the American public. By equitable data, we mean data that allow for rigorous assessment of the extent to which government programs and policies yield consistently fair, just, and impartial treatment of all individuals, including those who have been historically underserved, marginalized, and adversely affected by persistent poverty and inequality.

Fair data can bring attention to opportunities for targeted actions that will lead to noticeable improved results for marginalized communities. On essential characteristic of fair data is its disaggregation by demographic factors ( e.g., race, ethnicity, gender, language spoken, etc.), geographic factors ( e.g., rural/urban), or other variables, which allow for insights to disparities in access to and outcomes from government programs, policies and services

Establishing a robust and fair data infrastructure requires developing collaborations among various levels of government, as well as with a diverse array of external organizations, in order to advance outcomes for underserved communities. Building such infrastructure will likely need new incentives and avenues, including promoting greater data sharing and capacity building across different levels of government and expanding the research community engaged in producing and analyzing fair data. 

Moreover, it is essential to provide tools that enable civil society organizations and communities to utilize and visualize federal data and track the government’s progress towards achieving fair outcomes. This is crucial for enhancing accountability and trustworthiness with the American public.These tools should encourage community involvement in government equity initiatives, but they must be designed and implemented in a manner that aligns with the data analysis skills and resources available to community members. Ideally, these tools should allow the public to easily access meaningful and actionable data about the well-being of their communities and services provided to them. 

Source: Office of Science and Technology Policy. (2022). Request for Information; Equitable Data Engagement and Accountability (107th ed., Vol. 87, pp. 54269–54270). Federal Register. https://www.govinfo.gov/content/pkg/FR-2022-09-02/pdf/2022-19007.pdf

Spain

Digital Rights Governance Framework

Description: 

Digital technologies influence change at a fast pace in society, and might have harmful impact on individuals and communities. Against this backdrop, cities need enhanced models of governance to manage opportunities and risks driven by technology and ensure digital rights, which ultimately are human rights in the digital space, are protected and promoted. The proposal for the Digital Rights Governance Framework is a normative, yet pragmatic framework for the city-wide implementation of digital rights that unfolds the foundations, structures and tools necessary for developing a rights-based governance of the digitalisation of municipal services. It is established through (1) determining a city’s core values, (2) translating these core values into thematic areas (e.g transparency, autonomy, equity and participation), (3) combing both core values and thematic areas and choose a digital human rights understanding in the format of a bill of digital rights, a data-policy with sovereignty or a code of ethics.

Methods for public engagement and community participation:

●      Establishment of civil society groups and representatives who are willing to engage in consultations and actively participate in initiatives 

●      (Short-term projects) Arrangement of public consultations and focus groups to involve city residents which involve designing and implementing new strategies. 

●      (Long term projects) Arrangement for representatives and civil society organisations to be involved in policy developments, to ensure marginalised voices are heard. 

●      Establishment of a system for civil society organisation to promptly inform municipalities for urgent manners. 

●      Development of a comprehensive inventory of potential digital rights “violations” in the city, engage in public dialogues, and seek to put on strategies for addressing them. 

●      Community Manager to facilitate the contact with these stakeholders.

●      Organisation of public consultations on new and impactful digital policies.

Source: Cities Coalition for Digital Rights, & Un Habitat. (n.d.). DIGITAL RIGHTS GOVERNANCE FRAMEWORK. https://citiesfordigitalrights.org/sites/default/files/DIGITAL%20RIGHTS%20FRAMEWORK_CONCEPT%20FOR%20FEEDBACK.pdf

USA

Equitable Data Engagement and Accountability

Description: 

In its final report in April 2022— Vision for Equitable Data —the Equitable Data Working Group emphasised the need for the Federal government to use equitable data to:

1. Encourage diverse collaborations across levels of government, civil society, and the research community
2. Be accountable to the American public. By equitable data, we mean data that allow for rigorous assessment of the extent to which government programs and policies yield consistently fair, just, and impartial treatment of all individuals, including those who have been historically underserved, marginalized, and adversely affected by persistent poverty and inequality.

Fair data can bring attention to opportunities for targeted actions that will lead to noticeable improved results for marginalized communities. On essential characteristic of fair data is its disaggregation by demographic factors ( e.g., race, ethnicity, gender, language spoken, etc.), geographic factors ( e.g., rural/urban), or other variables, which allow for insights to disparities in access to and outcomes from government programs, policies and services

Establishing a robust and fair data infrastructure requires developing collaborations among various levels of government, as well as with a diverse array of external organizations, in order to advance outcomes for underserved communities. Building such infrastructure will likely need new incentives and avenues, including promoting greater data sharing and capacity building across different levels of government and expanding the research community engaged in producing and analyzing fair data. 

Moreover, it is essential to provide tools that enable civil society organizations and communities to utilize and visualize federal data and track the government’s progress towards achieving fair outcomes. This is crucial for enhancing accountability and trustworthiness with the American public.These tools should encourage community involvement in government equity initiatives, but they must be designed and implemented in a manner that aligns with the data analysis skills and resources available to community members. Ideally, these tools should allow the public to easily access meaningful and actionable data about the well-being of their communities and services provided to them. 

Source: Office of Science and Technology Policy. (2022). Request for Information; Equitable Data Engagement and Accountability (107th ed., Vol. 87, pp. 54269–54270). Federal Register. https://www.govinfo.gov/content/pkg/FR-2022-09-02/pdf/2022-19007.pdf

UK, USA, Singapore

How digital transformation can be made good for all

Description:

The aim of this project investigated the associated risks in locating digital transformation strategies outside communities, as well as their preferences and priorities when moving into a digital environment. Specifically, the project theories that top-down approaches to digital transformation and digital readiness can overlook important demographic differences in communities in areas of digital literacy, digital familiarity, access to technology, and consent. 

The project initially looks at case studies in the UK and Singapore and the characteristics of vulnerable recipients in digital transformation, critiquing top-down approaches. Secondly, it examines current examples of ‘co-creation policies’ that can be found in the Chicago police community. This allows for the identification of understanding effective methods for citizen participation and obstacles to bottom-up policy development when disabled or disadvantaged communities are included. Lastly, the concept of Living Digital Transformation (LDT) is introduced and examined in terms of how bottom-up and user-centric digitising is a more sustainable and inclusive approach, in the context of university communities. 

Source: Findlay, M., & Shanmugam, S. (2023). Participatory Digital Futures: How digital transformation can be made good for all. SSRN, 2. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430120

USA

Keating Memorial Self-Research

Description

The Open Humans Foundation is a nonprofit organisation dedicated to empowering individuals and communities around their personal data, to explore and share for the purposes of education, health, and research. The organisation operates and manages Open Humans, a project and community that enables individuals to connect their data with research and citizen science.

The Keating Memorial Self-Research encourages people to share their ideas for self-research projects with the community so different people can work on the project and share their data to contribute to the project´s analysis. The project uses a participatory research methodology, specifically a citizen science approach, to engage individuals in the collection, sharing, and analysis of personal health data. This approach involves the voluntary contribution of personal data by individuals to research studies, providing tools and resources for data management and analysis, and collaboration with researchers and other participants.

The mechanisms to engage the people in the different projects are through a monthly community call, focused on discussing topics of interest, management tools and potential collaborations, and weekly self-research chats, to discuss the self-research projects, progress, and insights. 

Through Open Humans, individuals can gain control over their personal health data, and participate in research that is aligned with their values and needs. The project emphasizes community engagement, transparency, and empowerment, and seeks to promote more inclusive and collaborative models of research.

Results: 

184 people joined the Keating Memorial project but no activity has been registered since 2021. However, the Open Humans platform has more than 30 ongoing projects related to different topics like genome, urban mobility, and different types of diseases- where people can share and explore their data. 

Source: Open Humans. (n.d.). About – Open Humans. Www.openhumans.org. Retrieved May 11, 2023, from https://www.openhumans.org/about/

USA

The Data Assembly in New York

Description: 

The Data Assembly began in the summer of 2020 with an initial focus on the response to the COVID-19 pandemic in New York City. Remote deliberations with three “mini-publics” composed of data holders, policymakers, representatives of civic rights, advocacy organisations, and citizens were held to create recommendations to guide the data-driven response to COVID-19 and other emerging threats.

Results: 

The recommendations from the discussions are the following

●      Match urgency with accountability: participants agreed that in case of a health emergency, they would accept increased surveillance but the organisations should comply with mechanisms that guarantee public accountability. 

●      Support and expand data literacy: clear communication is important for different stakeholders to understand the benefits of data reuse. 

●      Centre equity: To avoid reproducing existing inequalities, organisations should assess if their data intended to be reused misrepresent any population or can cause any harm. 

●      Engage legitimate, local actors: participants highlighted the need to include local actors, especially organisations working at the local level 

●      Develop positions for responsible data reuse: there is a need for data stewards that can help organisations manage their data and coordinate the interactions with the different stakeholders in the data sphere. 

Source: Young, A., Verhulst, S. G., Safonova, N., & Zahuranec, A. J. (2020). Responsible Data Re-Use Framework. In The Data Assembly. https://thedataassembly.org/files/nyc-data-assembly-report.pdf

UK

Control Access to Patient Records for Research in the UK

Description: 

The secondary use of health data for research raises complex questions of privacy and governance. Such questions are ill-suited to opinion polling where citizens must choose quickly between multiple-choice answers based on little information. Two citizens´ juries, of 17 citizens each, were convened for three days to reflect on what control informed citizens would seek over the use of health records for research. Each jury answered, “To what extent should patients control access to patient records for secondary use?”. Jurors heard from and questioned five expert witnesses. Their individual views were polled using questionnaires at the beginning and at the end of the process.

Results: 

33 out of 34 jurors voted in support of the secondary use of data for research, with 24 wanting individuals to be able to opt out, six favoring opt-in, and three voting that all records should be available without any consent process. When considering who should get access to data, both juries had very similar rationales. Both thought that public benefit was a key justification for access. Jury 1 was more strongly supportive of sharing patient records for public benefit, in contrast, jury 2 was more cautious and sought to give patients more control.

The findings show that, when informed of both risks and opportunities associated with data sharing, citizens believe an individual’s right to privacy should not prevent research that can benefit the general public. 

Source: Tully, M. P., Bozentko, K., Clement, S., Hunn, A., Hassan, L., Norris, R., Oswald, M., & Peek, N. (2018). Investigating the Extent to Which Patients Should Control Access to Patient Records for Research: A Deliberative Process Using Citizens’ Juries (2nd ed., Vol. 20). Journal of Medical Internet Research. https://www.jmir.org/2018/3/e112

UK

Explainable AI in the UK

Description: 

Two juries were selected, one in Coventry and one in Manchester, with the purpose of analysing the importance the public gives to receiving an explanation when AI was used in their healthcare, focusing on the tradeoff between the accuracy and explainability of AI systems. The jurors were made up of a cross-section of the population, representing the demographic breakdown of England as per the 2011 Census. In total, 36 individuals were selected to be jurors. They considered the tradeoff accuracy-explainability in four different scenarios: 

1. Healthcare: diagnosis of acute stroke 
2. Healthcare: finding matches between kidney transplant donors and recipients 
3. Criminal Justice: deciding which offenders should be referred to a rehabilitation programme 
4. Recruitment: screening job applications and making shortlisting decisions

Results: 

Three key themes relating to explaining AI decisions emerged from the research:

1. The importance of context for the relevance of the explanation required
2. The need for education and awareness on the use of AI for decision-making
3. The challenges in providing explanations of AI at the expense of less accurate decision-making

Most jurors felt that the relative importance of explanations and accuracy varied by context. In contexts where humans would usually provide an explanation, most jurors indicated that explanations of AI decisions should be similar to human explanations. Jurors felt this was important to help build trust and to ensure explanations were understandable.

Source: Information Commissioner’s Office. (2019). Project ExplAIn Interim Report. https://ico.org.uk/media/2615039/project-explain-20190603.pdf

UK

Fair uses of NHS patients´ data and NHS operational data

Description:

A public engagement program was held to understand what people think about the NHS allowing third parties to access their health data, ranging from academics, charities, or industries. The analysis centred on the system of rules and environment that would make a health system trustworthy. 

 The mixed methods used were:

1. Three round tables involving patient representatives in Oxford, Manchester, and London: their aim was to design the stimulus materials and the method of testing the charge question for the Citizen’s Jury. There were a total of 30 patient representatives. 
2. Citizen’s Juries in Taunton, Leeds, and London:  to discuss the question, ‘What constitutes a fair partnership between the NHS and researchers, charities and industry on uses of NHS patients’ data and NHS operational data?’. This involved 60 jurors over two and a half days.
3. A nationally representative survey from the UK: 2095 people completed the survey. It quantitatively explored important topics jurors focused on and tested broader public opinion on several key themes that emerged including the level of awareness of data access partnerships in a representative sample and aspects of communication raised by jurors. 

Results:

Specifically looking at the results around citizens’ involvement the jurors decided that citizens need to be more involved at different decision-making stages, especially in policy and practices. Meaning, citizens should be present and encouraged to participate in the process of establishing and managing data access partnerships. The jurors identified three engagement management processes that can be used to include citizens: 

1. Citizens´ Juries and deliberation for key decisions
2. Public votes to approve local partnerships
3. Playing a role in governance boards

Moreover, jurors proposed that each data access partnership should publish reports and case studies to provide transparency to the public. Moreover, jurors concluded that communities and individuals would be more resistant to data access partnerships using their data if procedures and methodologies are unclear. To increase trust, it is essential to create an awareness campaign about the national data opt-out service to increase trust and confidence in the system. 

Source: Hopkins, H., Kinsella, S., Van, A., Hopkins, M., & Mil, V. (2020). Foundations of fairness: views on uses of NHS patients’ data and NHS operational data A mixed methods public engagement programme with integrated Citizens’ Juries Findings Report. https://understandingpatientdata.org.uk/sites/default/files/2020-03/Foundations%20of%20Fairness%20-%20Full%20Research%20Report.pdf

UK

The ethical, legal, and social implications of data governance

The ethical, legal, and social implications of data governance during pandemics in the UK

Description: 

Two-week-long online citizens’ juries were organised to identify best practices for ensuring transparent, accountable, and inclusive data governance in the UK. A total of 50 citizens were recruited as jurors representing the demographic diversity of the UK population according to age, gender, ethnicity, and region. The jurors discussed data-driven technologies that played roles in the UK’s response to COVID-19, including vaccine passports, risk-scoring algorithms, and the GDPR. They were tasked with addressing the following questions: 

1. What constitutes good governance of data-driven technologies?  
2. What constitutes proportionate uses of data-driven technologies during pandemics? 

Results: 

These are the main conclusions from the jurors:

1. Transparency, communication, and clarity: there must be clear and consistent communication around the use of data-driven approaches, and the application of rules and public health measures during a pandemic.
2. Accountability: there must be an emphasis on adherence to the rule of law, protecting democracy, and ensuring robust, fair and equal enforcement of policies.
3. Equity, inclusiveness, and non-discrimination: the use of data-driven technologies should not exacerbate unequal social stratification.
4. Protection of personal freedoms: use of data-driven technologies should respect and protect individual liberties and rights.
5. Proportionate and time-limited uses: data use must balance public health needs and risks to individuals and society, and pandemic response measures must not extend into post-pandemic data futures.
6. Emergency preparedness and planning: effective, accurate, and responsibly managed data should be the basis for evidence and learning during emergency preparedness and planning.
7. Trustworthiness: the organisations and governance structures involved in the design and use of a data-driven technology must be trustworthy.

Source: Ada Lovelace Institute. (2022, July). The rule of trust. Www.adalovelaceinstitute.org. https://www.adalovelaceinstitute.org/report/trust-data-governance-pandemics/

USA

Keating Memorial Self-Research

Description

The Open Humans Foundation is a nonprofit organisation dedicated to empowering individuals and communities around their personal data, to explore and share for the purposes of education, health, and research. The organisation operates and manages Open Humans, a project and community that enables individuals to connect their data with research and citizen science.

The Keating Memorial Self-Research encourages people to share their ideas for self-research projects with the community so different people can work on the project and share their data to contribute to the project´s analysis. The project uses a participatory research methodology, specifically a citizen science approach, to engage individuals in the collection, sharing, and analysis of personal health data. This approach involves the voluntary contribution of personal data by individuals to research studies, providing tools and resources for data management and analysis, and collaboration with researchers and other participants.

The mechanisms to engage the people in the different projects are through a monthly community call, focused on discussing topics of interest, management tools and potential collaborations, and weekly self-research chats, to discuss the self-research projects, progress, and insights. 

Through Open Humans, individuals can gain control over their personal health data, and participate in research that is aligned with their values and needs. The project emphasizes community engagement, transparency, and empowerment, and seeks to promote more inclusive and collaborative models of research.

Results: 

184 people joined the Keating Memorial project but no activity has been registered since 2021. However, the Open Humans platform has more than 30 ongoing projects related to different topics like genome, urban mobility, and different types of diseases- where people can share and explore their data. 

Source: Open Humans. (n.d.). About – Open Humans. Www.openhumans.org. Retrieved May 11, 2023, from https://www.openhumans.org/about/

UK

Care.data

Description: 

The program care.data was made public in 2013 by the Health and Social Care Information Centre. The goal was to use data collected by GP surgeries and store them in a central database using the General Practice Extraction Service (GPES). Those who were part of GP practices were instructed that their health information would be stored in the database. Members of the GP practices who did not want their information to be uploaded were allowed to object. 

In this database, patients’ data was anonymized and could be accessed by health care researchers, managers and planners within the NHS, as well as academic institutions and organizations. The software and services used for this program were provided by Atos. 

The care.data program was heavily criticized and was deemed controversial since it was launched due to many overlooked factors. It was first critiqued around the lack of patient awareness regarding the program as well as lacking obvious options for objecting in the leaflet sent to homes. Additionally, the leaflet only described the program and the benefits surrounding it but did not include the opt-out form. 

After multiple reviews, the care.data program ended due to “major issues with project definition, schedule, budget, quality and/or benefits delivery, which at this stage do not appear to be manageable or resolvable” (Ramesh, 2015). Moreover, in December 2015 Atos was criticized by the Public Accounts Committee and was accused of taking advantage of the Department of Health. After a few years, the program ended in July 2016. 

The authors of The social license for research: why care.data ran into trouble argue that despite obtaining a lawful infrastructure for the implementation of the program, no social license was secured which led to challenges. 

Care.data could have been successful if three areas were recognized:

1. Establishing trust and confidence in the governance of research needs to go beyond focusing on economic gains and also consider the patient’s concerns as an individual seeking care. Meaning, their concerns as a citizen, who is part of a larger social fabric, is different to the concerns as an individual patient. 
2. In order for initiatives like care.data to be successful, patients must have confidence that their medical records will be securely and appropriately managed, with consideration of anonymization and public interest. 
3. Respecting the conditions of the social license involves upholding principles such as reciprocity, avoiding exploitation and prioritizing the public good.  

Source: Carter, P., Laurie, G. T., & Dixon-Woods, M. (2015). The social licence for research: whycare.dataran into trouble. Journal of Medical Ethics, 41(5), 404–409. https://doi.org/10.1136/medethics-2014-102374

Additional Links: 

●      Ramesh, R. (2015, June 26). NHS patient data plans unachievable, review finds. The Guardian. https://www.theguardian.com/politics/2015/jun/26/nhs-patient-data-plans-unachievable-review-health

●      Wikipedia . (2020, July 31). Care.data. Wikipedia. https://en.wikipedia.org/wiki/Care.data

Germany

Citizen Engagement and Innovative Data Use for Africa’s Development (DataCipation)

Description: 

The programme supports AU Organs in their quest to intensify citizen engagement and the role of data, digital and non-digital approaches in their programmes and initiatives. The programme is implemented in cooperation with the African Union Commission and the AU Development Agency (AUDA-NEPAD). The political partner for the programme is the Bureau of the Chairperson at the African Union Commission, demonstrating the high political commitment of the AU to the programme.

The programme takes a systemic approach, focusing on implementation across three main areas as follows:

1. Connecting policymakers with Africa’s data and digital innovators for good governance and development by enhancing the collaboration and cooperation of the AU Organs and Member States with Africa’s digital innovation ecosystem.
2. Improving citizen participation in good governance and development through innovative communications and engagement methodologies; leveraging data and digital and non-digital approaches.
3. Supporting the implementation of digital policies across Africa to improve access to meaningful participation of citizens in the digital transformation and to exploit the related potentials for social and economic development

Success factors included, enabling the African Union Commission to lead by example for digital transformation in public sector innovation as well as interactive, participatory communications efforts.Secondly it allowed for the building coalitions of the willing of AU Member States for spearheading novel digital policy approaches that pave the way for broader adoption at continental level. And lastly it permitted for a trusted partner to AUC and African policymakers by providing independent expertise geared towards realising the strategic interests including techno-geopolitical sovereignty of the African continent. 

Source: giz. (2021, December). Citizen Engagement and Innovative Data Use for Africa’s Development (DataCipation). Www.giz.de. https://www.giz.de/en/worldwide/98533.html

USA

Equitable Data Engagement and Accountability

Description: 

In its final report in April 2022— Vision for Equitable Data —the Equitable Data Working Group emphasised the need for the Federal government to use equitable data to:

1. Encourage diverse collaborations across levels of government, civil society, and the research community
2. Be accountable to the American public. By equitable data, we mean data that allow for rigorous assessment of the extent to which government programs and policies yield consistently fair, just, and impartial treatment of all individuals, including those who have been historically underserved, marginalized, and adversely affected by persistent poverty and inequality.

Fair data can bring attention to opportunities for targeted actions that will lead to noticeable improved results for marginalized communities. On essential characteristic of fair data is its disaggregation by demographic factors ( e.g., race, ethnicity, gender, language spoken, etc.), geographic factors ( e.g., rural/urban), or other variables, which allow for insights to disparities in access to and outcomes from government programs, policies and services

Establishing a robust and fair data infrastructure requires developing collaborations among various levels of government, as well as with a diverse array of external organizations, in order to advance outcomes for underserved communities. Building such infrastructure will likely need new incentives and avenues, including promoting greater data sharing and capacity building across different levels of government and expanding the research community engaged in producing and analyzing fair data. 

Moreover, it is essential to provide tools that enable civil society organizations and communities to utilize and visualize federal data and track the government’s progress towards achieving fair outcomes. This is crucial for enhancing accountability and trustworthiness with the American public.These tools should encourage community involvement in government equity initiatives, but they must be designed and implemented in a manner that aligns with the data analysis skills and resources available to community members. Ideally, these tools should allow the public to easily access meaningful and actionable data about the well-being of their communities and services provided to them. 

Source: Office of Science and Technology Policy. (2022). Request for Information; Equitable Data Engagement and Accountability (107th ed., Vol. 87, pp. 54269–54270). Federal Register. https://www.govinfo.gov/content/pkg/FR-2022-09-02/pdf/2022-19007.pdf

UK, USA, Singapore

How digital transformation can be made good for all

Description:

The aim of this project investigated the associated risks in locating digital transformation strategies outside communities, as well as their preferences and priorities when moving into a digital environment. Specifically, the project theories that top-down approaches to digital transformation and digital readiness can overlook important demographic differences in communities in areas of digital literacy, digital familiarity, access to technology, and consent. 

The project initially looks at case studies in the UK and Singapore and the characteristics of vulnerable recipients in digital transformation, critiquing top-down approaches. Secondly, it examines current examples of ‘co-creation policies’ that can be found in the Chicago police community. This allows for the identification of understanding effective methods for citizen participation and obstacles to bottom-up policy development when disabled or disadvantaged communities are included. Lastly, the concept of Living Digital Transformation (LDT) is introduced and examined in terms of how bottom-up and user-centric digitising is a more sustainable and inclusive approach, in the context of university communities. 

Source: Findlay, M., & Shanmugam, S. (2023). Participatory Digital Futures: How digital transformation can be made good for all. SSRN, 2. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430120

UK

Belfast to launch “Citizen Office of Digital Innovation”

Description:

In 2022, Belfast, located in Northern Ireland launched and piloted a program called Citizen Office of Digital Innovation (CODI). It aimed to increase resident engagement when it comes to data and technology. Overall, the program looks to support ‘digital citizenship skills’ which are part of the Smart Belfast Programme. This looks to help citizens to develop a better understanding of how technology is utilized in Belfast. Within the program, creative and interactive methods of engagement were used to explore concepts like co-design, citizen science, the Internet of Things, Artificial Intelligence, data, science, and privacy.  

Source: Wray, S. (Ed.). (2022, September 1). Belfast to launch “Citizen Office of Digital Innovation.” Cities Today. https://www.itu.int/hub/2022/09/belfast-to-launch-citizen-office-of-digital-innovation/

UK, India, South Africa

Mindkind: Global mental health databank pilot

Description: 

Over the course of two years, Wellcome trust developed a pilot project which aimed at exploring the governance of data related to mental health. They joined with Sage Bionetworks to create a prototype and test the ability to build a global mental health databank (GMHD). This databank included longitudinal and electronically-derived data from youth, with an emphasis on the approaches, treatments, and interventions potentially relevant to anxiety or depression. To develop the project, a participatory data governance process was implemented involving youth aged between 14-24 in the decision-making process of the design, collection, sharing, analysis, reuse, and use of data by the researchers. The following participatory mechanisms were created: 

1. Young People’s Advisory Group: Each country had a Young People’s Advisory Group that met regularly online to discuss the study design and data collection. In total there were 32 participants from a target group of young people aged 16-24 with lived experience of mental health challenges.
2. Professional Youth Advisor: Each country recruited a full-time youth advisor as a member of the study team. Their role was to act as a link between the Young People’s Advisory Group and the project teams and support research directions. In total there were 3 professional youth advisors.
3. Global Youth Panel: The panel was composed by members with past experience on youth panels and advocacy groups for mental health issues. They met monthly to provide high-level feedback on project decisions. There were 15 participants in the panel.
4. International Youth Panel: Due to delays in the recruitment of young people for the Young People’s Advisory group, this panel was created ad hoc. The objective of this panel was to identify “active ingredients” of mental health, map data governance models and data collection strategies. 
5. Data Use Advisory Group: This group was responsible for providing scientific insights on the use of a global mental health databank and discussing its ethical issues. The members were from 7 countries (Australia, Brazil, India, Nigeria, South Africa, United Kingdom, and USA). There were 18 participants in the group.
6. Randomised Control Trial: A RCT was designed with participants from India, South Africa and the UK to test the governance conditions of the MindKind app after recording information about their mental health and behaviours. The sample was of 300 participants from a target group of young people that at least 10% had experienced mental health challenges.
7. Deliberative Democracy Sessions: Two rounds of deliberative democracy sessions were held in each country. There were in total 150 participants discussing different data governance models, benefits and concerns around data governance for a global mental health databank. 

Results: There was no statistically significant difference in enrollment rates across data governance models, even though the study expected that youngsters will prefer governance models that provide them more control over their data, they enrolled in the program regardless of the control over how their data was used or accessed. Moreover, participants given a choice of study topics showed a statistically significantly lower engagement with the study than participants who were assigned fixed study topics.

Source: 

Connected by Data. (n.d.). Connected by data | MindKind: Global mental health databank pilot. Connectedbydata.org. Retrieved May 11, 2023, from http://connectedbydata.org/cases/mindkind-global-mental-health-databank-pilot

Spain

Digital Rights Governance Framework

Description: 

Digital technologies influence change at a fast pace in society, and might have harmful impact on individuals and communities. Against this backdrop, cities need enhanced models of governance to manage opportunities and risks driven by technology and ensure digital rights, which ultimately are human rights in the digital space, are protected and promoted. The proposal for the Digital Rights Governance Framework is a normative, yet pragmatic framework for the city-wide implementation of digital rights that unfolds the foundations, structures and tools necessary for developing a rights-based governance of the digitalisation of municipal services. It is established through (1) determining a city’s core values, (2) translating these core values into thematic areas (e.g transparency, autonomy, equity and participation), (3) combing both core values and thematic areas and choose a digital human rights understanding in the format of a bill of digital rights, a data-policy with sovereignty or a code of ethics.

Methods for public engagement and community participation:

●      Establishment of civil society groups and representatives who are willing to engage in consultations and actively participate in initiatives 

●      (Short-term projects) Arrangement of public consultations and focus groups to involve city residents which involve designing and implementing new strategies. 

●      (Long term projects) Arrangement for representatives and civil society organisations to be involved in policy developments, to ensure marginalised voices are heard. 

●      Establishment of a system for civil society organisation to promptly inform municipalities for urgent manners. 

●      Development of a comprehensive inventory of potential digital rights “violations” in the city, engage in public dialogues, and seek to put on strategies for addressing them. 

●      Community Manager to facilitate the contact with these stakeholders.

●      Organisation of public consultations on new and impactful digital policies.

Source: Cities Coalition for Digital Rights, & Un Habitat. (n.d.). DIGITAL RIGHTS GOVERNANCE FRAMEWORK. https://citiesfordigitalrights.org/sites/default/files/DIGITAL%20RIGHTS%20FRAMEWORK_CONCEPT%20FOR%20FEEDBACK.pdf

UK

Fair uses of NHS patients´ data and NHS operational data

Description:

A public engagement program was held to understand what people think about the NHS allowing third parties to access their health data, ranging from academics, charities, or industries. The analysis centred on the system of rules and environment that would make a health system trustworthy. 

 The mixed methods used were:

1. Three round tables involving patient representatives in Oxford, Manchester, and London: their aim was to design the stimulus materials and the method of testing the charge question for the Citizen’s Jury. There were a total of 30 patient representatives. 
2. Citizen’s Juries in Taunton, Leeds, and London:  to discuss the question, ‘What constitutes a fair partnership between the NHS and researchers, charities and industry on uses of NHS patients’ data and NHS operational data?’. This involved 60 jurors over two and a half days.
3. A nationally representative survey from the UK: 2095 people completed the survey. It quantitatively explored important topics jurors focused on and tested broader public opinion on several key themes that emerged including the level of awareness of data access partnerships in a representative sample and aspects of communication raised by jurors. 

Results:

Specifically looking at the results around citizens’ involvement the jurors decided that citizens need to be more involved at different decision-making stages, especially in policy and practices. Meaning, citizens should be present and encouraged to participate in the process of establishing and managing data access partnerships. The jurors identified three engagement management processes that can be used to include citizens: 

1. Citizens´ Juries and deliberation for key decisions
2. Public votes to approve local partnerships
3. Playing a role in governance boards

Moreover, jurors proposed that each data access partnership should publish reports and case studies to provide transparency to the public. Moreover, jurors concluded that communities and individuals would be more resistant to data access partnerships using their data if procedures and methodologies are unclear. To increase trust, it is essential to create an awareness campaign about the national data opt-out service to increase trust and confidence in the system. 

Source: Hopkins, H., Kinsella, S., Van, A., Hopkins, M., & Mil, V. (2020). Foundations of fairness: views on uses of NHS patients’ data and NHS operational data A mixed methods public engagement programme with integrated Citizens’ Juries Findings Report. https://understandingpatientdata.org.uk/sites/default/files/2020-03/Foundations%20of%20Fairness%20-%20Full%20Research%20Report.pdf

UK

Fair uses of NHS patients´ data and NHS operational data

Description:

A public engagement program was held to understand what people think about the NHS allowing third parties to access their health data, ranging from academics, charities, or industries. The analysis centred on the system of rules and environment that would make a health system trustworthy. 

 The mixed methods used were:

1. Three round tables involving patient representatives in Oxford, Manchester, and London: their aim was to design the stimulus materials and the method of testing the charge question for the Citizen’s Jury. There were a total of 30 patient representatives. 
2. Citizen’s Juries in Taunton, Leeds, and London:  to discuss the question, ‘What constitutes a fair partnership between the NHS and researchers, charities and industry on uses of NHS patients’ data and NHS operational data?’. This involved 60 jurors over two and a half days.
3. A nationally representative survey from the UK: 2095 people completed the survey. It quantitatively explored important topics jurors focused on and tested broader public opinion on several key themes that emerged including the level of awareness of data access partnerships in a representative sample and aspects of communication raised by jurors. 

Results:

Specifically looking at the results around citizens’ involvement the jurors decided that citizens need to be more involved at different decision-making stages, especially in policy and practices. Meaning, citizens should be present and encouraged to participate in the process of establishing and managing data access partnerships. The jurors identified three engagement management processes that can be used to include citizens: 

1. Citizens´ Juries and deliberation for key decisions
2. Public votes to approve local partnerships
3. Playing a role in governance boards

Moreover, jurors proposed that each data access partnership should publish reports and case studies to provide transparency to the public. Moreover, jurors concluded that communities and individuals would be more resistant to data access partnerships using their data if procedures and methodologies are unclear. To increase trust, it is essential to create an awareness campaign about the national data opt-out service to increase trust and confidence in the system. 

Source: Hopkins, H., Kinsella, S., Van, A., Hopkins, M., & Mil, V. (2020). Foundations of fairness: views on uses of NHS patients’ data and NHS operational data A mixed methods public engagement programme with integrated Citizens’ Juries Findings Report. https://understandingpatientdata.org.uk/sites/default/files/2020-03/Foundations%20of%20Fairness%20-%20Full%20Research%20Report.pdf

UK

Belfast to launch “Citizen Office of Digital Innovation”

Description:

In 2022, Belfast, located in Northern Ireland launched and piloted a program called Citizen Office of Digital Innovation (CODI). It aimed to increase resident engagement when it comes to data and technology. Overall, the program looks to support ‘digital citizenship skills’ which are part of the Smart Belfast Programme. This looks to help citizens to develop a better understanding of how technology is utilized in Belfast. Within the program, creative and interactive methods of engagement were used to explore concepts like co-design, citizen science, the Internet of Things, Artificial Intelligence, data, science, and privacy.  

Source: Wray, S. (Ed.). (2022, September 1). Belfast to launch “Citizen Office of Digital Innovation.” Cities Today. https://www.itu.int/hub/2022/09/belfast-to-launch-citizen-office-of-digital-innovation/

UK

Care.data

Description: 

The program care.data was made public in 2013 by the Health and Social Care Information Centre. The goal was to use data collected by GP surgeries and store them in a central database using the General Practice Extraction Service (GPES). Those who were part of GP practices were instructed that their health information would be stored in the database. Members of the GP practices who did not want their information to be uploaded were allowed to object. 

In this database, patients’ data was anonymized and could be accessed by health care researchers, managers and planners within the NHS, as well as academic institutions and organizations. The software and services used for this program were provided by Atos. 

The care.data program was heavily criticized and was deemed controversial since it was launched due to many overlooked factors. It was first critiqued around the lack of patient awareness regarding the program as well as lacking obvious options for objecting in the leaflet sent to homes. Additionally, the leaflet only described the program and the benefits surrounding it but did not include the opt-out form. 

After multiple reviews, the care.data program ended due to “major issues with project definition, schedule, budget, quality and/or benefits delivery, which at this stage do not appear to be manageable or resolvable” (Ramesh, 2015). Moreover, in December 2015 Atos was criticized by the Public Accounts Committee and was accused of taking advantage of the Department of Health. After a few years, the program ended in July 2016. 

The authors of The social license for research: why care.data ran into trouble argue that despite obtaining a lawful infrastructure for the implementation of the program, no social license was secured which led to challenges. 

Care.data could have been successful if three areas were recognized:

1. Establishing trust and confidence in the governance of research needs to go beyond focusing on economic gains and also consider the patient’s concerns as an individual seeking care. Meaning, their concerns as a citizen, who is part of a larger social fabric, is different to the concerns as an individual patient. 
2. In order for initiatives like care.data to be successful, patients must have confidence that their medical records will be securely and appropriately managed, with consideration of anonymization and public interest. 
3. Respecting the conditions of the social license involves upholding principles such as reciprocity, avoiding exploitation and prioritizing the public good.  

Source: Carter, P., Laurie, G. T., & Dixon-Woods, M. (2015). The social licence for research: whycare.dataran into trouble. Journal of Medical Ethics, 41(5), 404–409. https://doi.org/10.1136/medethics-2014-102374

Additional Links: 

●      Ramesh, R. (2015, June 26). NHS patient data plans unachievable, review finds. The Guardian. https://www.theguardian.com/politics/2015/jun/26/nhs-patient-data-plans-unachievable-review-health

●      Wikipedia . (2020, July 31). Care.data. Wikipedia. https://en.wikipedia.org/wiki/Care.data

Germany

Citizen Engagement and Innovative Data Use for Africa’s Development (DataCipation)

Description: 

The programme supports AU Organs in their quest to intensify citizen engagement and the role of data, digital and non-digital approaches in their programmes and initiatives. The programme is implemented in cooperation with the African Union Commission and the AU Development Agency (AUDA-NEPAD). The political partner for the programme is the Bureau of the Chairperson at the African Union Commission, demonstrating the high political commitment of the AU to the programme.

The programme takes a systemic approach, focusing on implementation across three main areas as follows:

1. Connecting policymakers with Africa’s data and digital innovators for good governance and development by enhancing the collaboration and cooperation of the AU Organs and Member States with Africa’s digital innovation ecosystem.
2. Improving citizen participation in good governance and development through innovative communications and engagement methodologies; leveraging data and digital and non-digital approaches.
3. Supporting the implementation of digital policies across Africa to improve access to meaningful participation of citizens in the digital transformation and to exploit the related potentials for social and economic development

Success factors included, enabling the African Union Commission to lead by example for digital transformation in public sector innovation as well as interactive, participatory communications efforts.Secondly it allowed for the building coalitions of the willing of AU Member States for spearheading novel digital policy approaches that pave the way for broader adoption at continental level. And lastly it permitted for a trusted partner to AUC and African policymakers by providing independent expertise geared towards realising the strategic interests including techno-geopolitical sovereignty of the African continent. 

Source: giz. (2021, December). Citizen Engagement and Innovative Data Use for Africa’s Development (DataCipation). Www.giz.de. https://www.giz.de/en/worldwide/98533.html

Spain

Digital Rights Governance Framework

Description: 

Digital technologies influence change at a fast pace in society, and might have harmful impact on individuals and communities. Against this backdrop, cities need enhanced models of governance to manage opportunities and risks driven by technology and ensure digital rights, which ultimately are human rights in the digital space, are protected and promoted. The proposal for the Digital Rights Governance Framework is a normative, yet pragmatic framework for the city-wide implementation of digital rights that unfolds the foundations, structures and tools necessary for developing a rights-based governance of the digitalisation of municipal services. It is established through (1) determining a city’s core values, (2) translating these core values into thematic areas (e.g transparency, autonomy, equity and participation), (3) combing both core values and thematic areas and choose a digital human rights understanding in the format of a bill of digital rights, a data-policy with sovereignty or a code of ethics.

Methods for public engagement and community participation:

●      Establishment of civil society groups and representatives who are willing to engage in consultations and actively participate in initiatives 

●      (Short-term projects) Arrangement of public consultations and focus groups to involve city residents which involve designing and implementing new strategies. 

●      (Long term projects) Arrangement for representatives and civil society organisations to be involved in policy developments, to ensure marginalised voices are heard. 

●      Establishment of a system for civil society organisation to promptly inform municipalities for urgent manners. 

●      Development of a comprehensive inventory of potential digital rights “violations” in the city, engage in public dialogues, and seek to put on strategies for addressing them. 

●      Community Manager to facilitate the contact with these stakeholders.

●      Organisation of public consultations on new and impactful digital policies.

Source: Cities Coalition for Digital Rights, & Un Habitat. (n.d.). DIGITAL RIGHTS GOVERNANCE FRAMEWORK. https://citiesfordigitalrights.org/sites/default/files/DIGITAL%20RIGHTS%20FRAMEWORK_CONCEPT%20FOR%20FEEDBACK.pdf

USA

Equitable Data Engagement and Accountability

Description: 

In its final report in April 2022— Vision for Equitable Data —the Equitable Data Working Group emphasised the need for the Federal government to use equitable data to:

1. Encourage diverse collaborations across levels of government, civil society, and the research community
2. Be accountable to the American public. By equitable data, we mean data that allow for rigorous assessment of the extent to which government programs and policies yield consistently fair, just, and impartial treatment of all individuals, including those who have been historically underserved, marginalized, and adversely affected by persistent poverty and inequality.

Fair data can bring attention to opportunities for targeted actions that will lead to noticeable improved results for marginalized communities. On essential characteristic of fair data is its disaggregation by demographic factors ( e.g., race, ethnicity, gender, language spoken, etc.), geographic factors ( e.g., rural/urban), or other variables, which allow for insights to disparities in access to and outcomes from government programs, policies and services

Establishing a robust and fair data infrastructure requires developing collaborations among various levels of government, as well as with a diverse array of external organizations, in order to advance outcomes for underserved communities. Building such infrastructure will likely need new incentives and avenues, including promoting greater data sharing and capacity building across different levels of government and expanding the research community engaged in producing and analyzing fair data. 

Moreover, it is essential to provide tools that enable civil society organizations and communities to utilize and visualize federal data and track the government’s progress towards achieving fair outcomes. This is crucial for enhancing accountability and trustworthiness with the American public.These tools should encourage community involvement in government equity initiatives, but they must be designed and implemented in a manner that aligns with the data analysis skills and resources available to community members. Ideally, these tools should allow the public to easily access meaningful and actionable data about the well-being of their communities and services provided to them. 

Source: Office of Science and Technology Policy. (2022). Request for Information; Equitable Data Engagement and Accountability (107th ed., Vol. 87, pp. 54269–54270). Federal Register. https://www.govinfo.gov/content/pkg/FR-2022-09-02/pdf/2022-19007.pdf

UK

Control Access to Patient Records for Research in the UK

Description: 

The secondary use of health data for research raises complex questions of privacy and governance. Such questions are ill-suited to opinion polling where citizens must choose quickly between multiple-choice answers based on little information. Two citizens´ juries, of 17 citizens each, were convened for three days to reflect on what control informed citizens would seek over the use of health records for research. Each jury answered, “To what extent should patients control access to patient records for secondary use?”. Jurors heard from and questioned five expert witnesses. Their individual views were polled using questionnaires at the beginning and at the end of the process.

Results: 

33 out of 34 jurors voted in support of the secondary use of data for research, with 24 wanting individuals to be able to opt out, six favoring opt-in, and three voting that all records should be available without any consent process. When considering who should get access to data, both juries had very similar rationales. Both thought that public benefit was a key justification for access. Jury 1 was more strongly supportive of sharing patient records for public benefit, in contrast, jury 2 was more cautious and sought to give patients more control.

The findings show that, when informed of both risks and opportunities associated with data sharing, citizens believe an individual’s right to privacy should not prevent research that can benefit the general public. 

Source: Tully, M. P., Bozentko, K., Clement, S., Hunn, A., Hassan, L., Norris, R., Oswald, M., & Peek, N. (2018). Investigating the Extent to Which Patients Should Control Access to Patient Records for Research: A Deliberative Process Using Citizens’ Juries (2nd ed., Vol. 20). Journal of Medical Internet Research. https://www.jmir.org/2018/3/e112

UK

Explainable AI in the UK

Description: 

Two juries were selected, one in Coventry and one in Manchester, with the purpose of analysing the importance the public gives to receiving an explanation when AI was used in their healthcare, focusing on the tradeoff between the accuracy and explainability of AI systems. The jurors were made up of a cross-section of the population, representing the demographic breakdown of England as per the 2011 Census. In total, 36 individuals were selected to be jurors. They considered the tradeoff accuracy-explainability in four different scenarios: 

1. Healthcare: diagnosis of acute stroke 
2. Healthcare: finding matches between kidney transplant donors and recipients 
3. Criminal Justice: deciding which offenders should be referred to a rehabilitation programme 
4. Recruitment: screening job applications and making shortlisting decisions

Results: 

Three key themes relating to explaining AI decisions emerged from the research:

1. The importance of context for the relevance of the explanation required
2. The need for education and awareness on the use of AI for decision-making
3. The challenges in providing explanations of AI at the expense of less accurate decision-making

Most jurors felt that the relative importance of explanations and accuracy varied by context. In contexts where humans would usually provide an explanation, most jurors indicated that explanations of AI decisions should be similar to human explanations. Jurors felt this was important to help build trust and to ensure explanations were understandable.

Source: Information Commissioner’s Office. (2019). Project ExplAIn Interim Report. https://ico.org.uk/media/2615039/project-explain-20190603.pdf

UK

Fair uses of NHS patients´ data and NHS operational data

Description:

A public engagement program was held to understand what people think about the NHS allowing third parties to access their health data, ranging from academics, charities, or industries. The analysis centred on the system of rules and environment that would make a health system trustworthy. 

 The mixed methods used were:

1. Three round tables involving patient representatives in Oxford, Manchester, and London: their aim was to design the stimulus materials and the method of testing the charge question for the Citizen’s Jury. There were a total of 30 patient representatives. 
2. Citizen’s Juries in Taunton, Leeds, and London:  to discuss the question, ‘What constitutes a fair partnership between the NHS and researchers, charities and industry on uses of NHS patients’ data and NHS operational data?’. This involved 60 jurors over two and a half days.
3. A nationally representative survey from the UK: 2095 people completed the survey. It quantitatively explored important topics jurors focused on and tested broader public opinion on several key themes that emerged including the level of awareness of data access partnerships in a representative sample and aspects of communication raised by jurors. 

Results:

Specifically looking at the results around citizens’ involvement the jurors decided that citizens need to be more involved at different decision-making stages, especially in policy and practices. Meaning, citizens should be present and encouraged to participate in the process of establishing and managing data access partnerships. The jurors identified three engagement management processes that can be used to include citizens: 

1. Citizens´ Juries and deliberation for key decisions
2. Public votes to approve local partnerships
3. Playing a role in governance boards

Moreover, jurors proposed that each data access partnership should publish reports and case studies to provide transparency to the public. Moreover, jurors concluded that communities and individuals would be more resistant to data access partnerships using their data if procedures and methodologies are unclear. To increase trust, it is essential to create an awareness campaign about the national data opt-out service to increase trust and confidence in the system. 

Source: Hopkins, H., Kinsella, S., Van, A., Hopkins, M., & Mil, V. (2020). Foundations of fairness: views on uses of NHS patients’ data and NHS operational data A mixed methods public engagement programme with integrated Citizens’ Juries Findings Report. https://understandingpatientdata.org.uk/sites/default/files/2020-03/Foundations%20of%20Fairness%20-%20Full%20Research%20Report.pdf

UK, USA, Singapore

How digital transformation can be made good for all

Description:

The aim of this project investigated the associated risks in locating digital transformation strategies outside communities, as well as their preferences and priorities when moving into a digital environment. Specifically, the project theories that top-down approaches to digital transformation and digital readiness can overlook important demographic differences in communities in areas of digital literacy, digital familiarity, access to technology, and consent. 

The project initially looks at case studies in the UK and Singapore and the characteristics of vulnerable recipients in digital transformation, critiquing top-down approaches. Secondly, it examines current examples of ‘co-creation policies’ that can be found in the Chicago police community. This allows for the identification of understanding effective methods for citizen participation and obstacles to bottom-up policy development when disabled or disadvantaged communities are included. Lastly, the concept of Living Digital Transformation (LDT) is introduced and examined in terms of how bottom-up and user-centric digitising is a more sustainable and inclusive approach, in the context of university communities. 

Source: Findlay, M., & Shanmugam, S. (2023). Participatory Digital Futures: How digital transformation can be made good for all. SSRN, 2. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430120

USA

Keating Memorial Self-Research

Description

The Open Humans Foundation is a nonprofit organisation dedicated to empowering individuals and communities around their personal data, to explore and share for the purposes of education, health, and research. The organisation operates and manages Open Humans, a project and community that enables individuals to connect their data with research and citizen science.

The Keating Memorial Self-Research encourages people to share their ideas for self-research projects with the community so different people can work on the project and share their data to contribute to the project´s analysis. The project uses a participatory research methodology, specifically a citizen science approach, to engage individuals in the collection, sharing, and analysis of personal health data. This approach involves the voluntary contribution of personal data by individuals to research studies, providing tools and resources for data management and analysis, and collaboration with researchers and other participants.

The mechanisms to engage the people in the different projects are through a monthly community call, focused on discussing topics of interest, management tools and potential collaborations, and weekly self-research chats, to discuss the self-research projects, progress, and insights. 

Through Open Humans, individuals can gain control over their personal health data, and participate in research that is aligned with their values and needs. The project emphasizes community engagement, transparency, and empowerment, and seeks to promote more inclusive and collaborative models of research.

Results: 

184 people joined the Keating Memorial project but no activity has been registered since 2021. However, the Open Humans platform has more than 30 ongoing projects related to different topics like genome, urban mobility, and different types of diseases- where people can share and explore their data. 

Source: Open Humans. (n.d.). About – Open Humans. Www.openhumans.org. Retrieved May 11, 2023, from https://www.openhumans.org/about/

UK, India, South Africa

Mindkind: Global mental health databank pilot

Description: 

Over the course of two years, Wellcome trust developed a pilot project which aimed at exploring the governance of data related to mental health. They joined with Sage Bionetworks to create a prototype and test the ability to build a global mental health databank (GMHD). This databank included longitudinal and electronically-derived data from youth, with an emphasis on the approaches, treatments, and interventions potentially relevant to anxiety or depression. To develop the project, a participatory data governance process was implemented involving youth aged between 14-24 in the decision-making process of the design, collection, sharing, analysis, reuse, and use of data by the researchers. The following participatory mechanisms were created: 

1. Young People’s Advisory Group: Each country had a Young People’s Advisory Group that met regularly online to discuss the study design and data collection. In total there were 32 participants from a target group of young people aged 16-24 with lived experience of mental health challenges.
2. Professional Youth Advisor: Each country recruited a full-time youth advisor as a member of the study team. Their role was to act as a link between the Young People’s Advisory Group and the project teams and support research directions. In total there were 3 professional youth advisors.
3. Global Youth Panel: The panel was composed by members with past experience on youth panels and advocacy groups for mental health issues. They met monthly to provide high-level feedback on project decisions. There were 15 participants in the panel.
4. International Youth Panel: Due to delays in the recruitment of young people for the Young People’s Advisory group, this panel was created ad hoc. The objective of this panel was to identify “active ingredients” of mental health, map data governance models and data collection strategies. 
5. Data Use Advisory Group: This group was responsible for providing scientific insights on the use of a global mental health databank and discussing its ethical issues. The members were from 7 countries (Australia, Brazil, India, Nigeria, South Africa, United Kingdom, and USA). There were 18 participants in the group.
6. Randomised Control Trial: A RCT was designed with participants from India, South Africa and the UK to test the governance conditions of the MindKind app after recording information about their mental health and behaviours. The sample was of 300 participants from a target group of young people that at least 10% had experienced mental health challenges.
7. Deliberative Democracy Sessions: Two rounds of deliberative democracy sessions were held in each country. There were in total 150 participants discussing different data governance models, benefits and concerns around data governance for a global mental health databank. 

Results: There was no statistically significant difference in enrollment rates across data governance models, even though the study expected that youngsters will prefer governance models that provide them more control over their data, they enrolled in the program regardless of the control over how their data was used or accessed. Moreover, participants given a choice of study topics showed a statistically significantly lower engagement with the study than participants who were assigned fixed study topics.

Source: 

Connected by Data. (n.d.). Connected by data | MindKind: Global mental health databank pilot. Connectedbydata.org. Retrieved May 11, 2023, from http://connectedbydata.org/cases/mindkind-global-mental-health-databank-pilot

UK

Public Acceptability of Data Sharing

Public Acceptability of Data Sharing Between the Public, Private, and Third Sectors for Research Purposes

Description: 

In 2012 the Scottish Government commissioned research to explore the public acceptability of cross-sectoral data linkage for research and statistical purposes to inform the ongoing development of a Scotland-wide Data Linkage Framework. The research indicated that the public was, in principle, broadly supportive of data linkage, particularly for health research, and of the overall objectives of the Data Linkage Framework. However, this support was conditional and a range of ambivalences and concerns were also expressed: there was significant unease about the private sector having access to public sector data and, more specifically, about the scope for commercial gain arising from data linkage. 

Report of a deliberative research project on the public’s attitudes toward data sharing. It focuses particularly on a) the public’s opinion about data sharing with the private and third sector; b) the acceptability of different methods for sharing benefits gained from the use of their data; and c) the appeal of different methods for empowering citizens in decision making about the use of their data. The study was conducted using a combination of primary and secondary research methods, comprising:

●      a desk-based literature review of international benefit-sharing models arising from the value of data sharing

●      a desk-based literature review of different methods that have been used to empower citizens in decision-making about how their data are used

●      a series of deliberative events with members of the public

Results: 

In the results, there is a dedicated section called ‘Empowering Citizens in Decision Making’. Within this section, the researchers determined the following: 

1. Participants in the discussion on public involvement in decision making regarding data sharing unanimously agreed that it was important and appropriate for the public to be involved in deciding how their data is used. 
2. When given 5 broad forms of public involvement: transparency, feedback, agenda-setting, informing policy, and representation, the most preferred formats decided by participants were transparency, feedback and informing policy.

 .      Transparency: participants wanted to know how their data are used and shared, including a rationale for why it is being shared, what type of data is being shared, how sharing works in practice and who will be able to access the data. 

 .      Feedback: this was important for informing the public about how research carried out using their data has benefited society. 

 .      Public involvement in policy-making: would allow the public, to a degree, have some control over how their data is used.

 .      Agenda-setting and representation: these were the least preferred methods as participants felt as if the public may know have the appropriate knowledge or expertise to contribute to this type of decision-making.

Source: The Scottish Government. (2013). Public Acceptability of Data Sharing Between the Public, Private and Third Sectors for Research Purposes. In The Scottish Government. https://www.google.com/url?q=https://www.gov.scot/publications/public-acceptability-data-sharing-between-public-private-third-sectors-research-purposes/&sa=D&source=docs&ust=1683813247138360&usg=AOvVaw345uiofE3iELOHd6LbG5Sz

USA

The Data Assembly in New York

Description: 

The Data Assembly began in the summer of 2020 with an initial focus on the response to the COVID-19 pandemic in New York City. Remote deliberations with three “mini-publics” composed of data holders, policymakers, representatives of civic rights, advocacy organisations, and citizens were held to create recommendations to guide the data-driven response to COVID-19 and other emerging threats.

Results: 

The recommendations from the discussions are the following

●      Match urgency with accountability: participants agreed that in case of a health emergency, they would accept increased surveillance but the organisations should comply with mechanisms that guarantee public accountability. 

●      Support and expand data literacy: clear communication is important for different stakeholders to understand the benefits of data reuse. 

●      Centre equity: To avoid reproducing existing inequalities, organisations should assess if their data intended to be reused misrepresent any population or can cause any harm. 

●      Engage legitimate, local actors: participants highlighted the need to include local actors, especially organisations working at the local level 

●      Develop positions for responsible data reuse: there is a need for data stewards that can help organisations manage their data and coordinate the interactions with the different stakeholders in the data sphere. 

Source: Young, A., Verhulst, S. G., Safonova, N., & Zahuranec, A. J. (2020). Responsible Data Re-Use Framework. In The Data Assembly. https://thedataassembly.org/files/nyc-data-assembly-report.pdf

UK

The ethical, legal, and social implications of data governance

The ethical, legal, and social implications of data governance during pandemics in the UK

Description: 

Two-week-long online citizens’ juries were organised to identify best practices for ensuring transparent, accountable, and inclusive data governance in the UK. A total of 50 citizens were recruited as jurors representing the demographic diversity of the UK population according to age, gender, ethnicity, and region. The jurors discussed data-driven technologies that played roles in the UK’s response to COVID-19, including vaccine passports, risk-scoring algorithms, and the GDPR. They were tasked with addressing the following questions: 

1. What constitutes good governance of data-driven technologies?  
2. What constitutes proportionate uses of data-driven technologies during pandemics? 

Results: 

These are the main conclusions from the jurors:

1. Transparency, communication, and clarity: there must be clear and consistent communication around the use of data-driven approaches, and the application of rules and public health measures during a pandemic.
2. Accountability: there must be an emphasis on adherence to the rule of law, protecting democracy, and ensuring robust, fair and equal enforcement of policies.
3. Equity, inclusiveness, and non-discrimination: the use of data-driven technologies should not exacerbate unequal social stratification.
4. Protection of personal freedoms: use of data-driven technologies should respect and protect individual liberties and rights.
5. Proportionate and time-limited uses: data use must balance public health needs and risks to individuals and society, and pandemic response measures must not extend into post-pandemic data futures.
6. Emergency preparedness and planning: effective, accurate, and responsibly managed data should be the basis for evidence and learning during emergency preparedness and planning.
7. Trustworthiness: the organisations and governance structures involved in the design and use of a data-driven technology must be trustworthy.

Source: Ada Lovelace Institute. (2022, July). The rule of trust. Www.adalovelaceinstitute.org. https://www.adalovelaceinstitute.org/report/trust-data-governance-pandemics/

UK

The use of data and statistics for “Public Good” in the UK

Description:

Administrative Data Research UK (ADR UK) and the Office for Statistics Regulation (OSR) partnered to explore public perceptions and understanding around the concept of what ‘public good’ means with regard to the use of data and statistics. These two organisations collaborated by developing a UK-wide public dialogue using workshops. Their overall aim was to develop a resource answering their primary question of “What are public perceptions around ‘public good’ use of data and statistics?’. Throughout these workshops, they explored the following sub-questions. 

1. How should ‘public good’ be defined and/or measured when making decisions about sharing data for research?
2. What uses of data and statistics are considered to be in the ‘public good?’
3. Are some uses of data and statistics ‘more’ in the public good than others? 
4. Are there conceptual differences between the phrases ‘public good and public interest’, public benefit, public welfare, common good, greater good, societal benefit, or other similar phrases (which are sometimes used interchangeably in the literature)?

Deliberative discussions were chosen as the method of engagement with 68 participants who lived in the UK. Four in-person workshops were held across London, Cardiff, Glasgow, and Belfast, and one workshop was held online for participants who were unable to attend in person. Additionally, an Advisory Board was created with individuals with relevant expertise to ensure important stakeholders were involved and appropriate dialogue was conducted.

Results: 

Here are the main recommendations identified by the participants:

1. Public involvement: citizens want to be involved and informed on how data in research and statistics are being used to serve the public good. Participants suggested that inclusive panels and public conversations should be held for the decision-making about data and statistics.
2. Real-world needs: citizens agree that research and statistics should aim to address the most pressing world needs, especially social inequity and social inequality. Participants recommended that the public have access to the decision-making process of Data Access Committees to understand the impact of proposed projects. 
3. Clear communication: participants recognize the importance of proactive communication that is clear and accessible that creates awareness of the importance, the motivations, and the outcomes of public good use of data for research and statistics
4. Minimize harm: data should not contribute to anything harmful, especially its use should avoid perpetuating stereotypes of certain groups of people. The public suggested consulting citizens with lived experience about potential uses of data or the interpretation of statistical patterns. Moreover, the participants agreed on the importance of increasing accountability from the experts working with data and statistics. 
5. Best practice safeguarding: citizens identified the importance of a framework, such as the Five Safes, that can be universally used to feel confident that public sector data is being used in a way they can trust. 

Source: Harkness , F., Rijneveld, C., Liu, Y., Kashef, S., & Cowan, M. (2022). A UK-wide public dialogue exploring what the public perceive as “public good” use of data for research and statistics. https://www.adruk.org/fileadmin/uploads/adruk/Documents/PE_reports_and_documents/ADR_UK_OSR_Public_Dialogue_final_report_October_2022.pdf