Practical Data Stewardship

Plotkin, David

doi:10.1016/b978-0-12-822132-7.00006-1

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We propose, therefore, that stronger protections are needed to ensure that when individuals use online services they are not risking their privacy. In seeking to enhance safeguards, individual autonomy can and should be respected through democratic deliberations with the public regarding their privacy preferences (see, e.g., methodologies of informed public dialogue by The Ada Lovelace Institute [50,51,102]; also Hartman et al [45]). This study really underlines that these preferences should be uncovered through consultation, rather than assuming that what people are willing to consent to maps to their actual preferences.…”

Section: Discussionmentioning

confidence: 99%

Un-Paradoxing Privacy: Considering Hopeful Trust

Knowles

Conchie

2023

ACM Trans. Comput.-Hum. Interact.

View full text Add to dashboard Cite

Extant literature has proposed an important role for trust in moderating people’s willingness to disclose personal information, but there is scant HCI literature that deeply explores the relationship between privacy and trust in apparent privacy paradox circumstances. Attending to this gap, this paper reports a qualitative study examining how people account for continuing to use services that conflict with their stated privacy preferences, and how trust features in these accounts. Our findings undermine the notion that individuals engage in strategic thinking about privacy, raising important questions regarding the explanatory power of the well-known privacy calculus model and its proposed relationship between privacy and trust. Finding evidence of hopeful trust in participants’ accounts, we argue that trust allows people to morally account for their ‘paradoxical’ information disclosure behavior. We propose that effecting greater alignment between people’s privacy attitudes and privacy behavior—or ‘un-paradoxing privacy’—will require greater regulatory assurances of privacy.

show abstract

Section: Discussionmentioning

confidence: 99%

Un-Paradoxing Privacy: Considering Hopeful Trust

Knowles

Conchie

2023

ACM Trans. Comput.-Hum. Interact.

View full text Add to dashboard Cite

show abstract

“…The executive group will conduct a targeted literature review of existing literature, guidelines, and toolkits that inform the aims of this Delphi study and will form the preparatory step for the initial Delphi questionnaire. There are two primary foci for the Delphi study that require a review of literature and other research outputs: (a) how to capture SOGI data (so it is complete, valid, and collected in an affirming way) and (b) the parameters of this data's reuse (similar to responsible data stewardship for AI and data-driven technologies 62 ).…”

Section: Stage 1: Defining the Terms-of-reference And Literature Reviewmentioning

confidence: 99%

Defining acceptable data collection and reuse standards for queer artificial intelligence research in mental health: protocol for the online PARQAIR-MH Delphi study

Joyce,

Kormilitzin,

Hamer-Hunt

et al. 2024

BMJ Open

View full text Add to dashboard Cite

IntroductionFor artificial intelligence (AI) to help improve mental healthcare, the design of data-driven technologies needs to be fair, safe, and inclusive. Participatory design can play a critical role in empowering marginalised communities to take an active role in constructing research agendas and outputs. Given the unmet needs of the LGBTQI+ (Lesbian, Gay, Bisexual, Transgender, Queer and Intersex) community in mental healthcare, there is a pressing need for participatory research to include a range of diverse queer perspectives on issues of data collection and use (in routine clinical care as well as for research) as well as AI design. Here we propose a protocol for a Delphi consensus process for the development of PARticipatory Queer AI Research for Mental Health (PARQAIR-MH) practices, aimed at informing digital health practices and policy.Methods and analysisThe development of PARQAIR-MH is comprised of four stages. In stage 1, a review of recent literature and fact-finding consultation with stakeholder organisations will be conducted to define a terms-of-reference for stage 2, the Delphi process. Our Delphi process consists of three rounds, where the first two rounds will iterate and identify items to be included in the final Delphi survey for consensus ratings. Stage 3 consists of consensus meetings to review and aggregate the Delphi survey responses, leading to stage 4 where we will produce a reusable toolkit to facilitate participatory development of future bespoke LGBTQI+–adapted data collection, harmonisation, and use for data-driven AI applications specifically in mental healthcare settings.Ethics and disseminationPARQAIR-MH aims to deliver a toolkit that will help to ensure that the specific needs of LGBTQI+ communities are accounted for in mental health applications of data-driven technologies. The study is expected to run from June 2024 through January 2025, with the final outputs delivered in mid-2025. Participants in the Delphi process will be recruited by snowball and opportunistic sampling via professional networks and social media (but not by direct approach to healthcare service users, patients, specific clinical services, or via clinicians’ caseloads). Participants will not be required to share personal narratives and experiences of healthcare or treatment for any condition. Before agreeing to participate, people will be given information about the issues considered to be in-scope for the Delphi (eg, developing best practices and methods for collecting and harmonising sensitive characteristics data; developing guidelines for data use/reuse) alongside specific risks of unintended harm from participating that can be reasonably anticipated. Outputs will be made available in open-access peer-reviewed publications, blogs, social media, and on a dedicated project website for future reuse.

show abstract

“…Very little is known about the data governance preferences of young people and their effect on willingness to participate in research. As the research community has increasingly embraced the importance of data sharing to accelerate and receive maximal value from research [4], data governance has tended to be crafted based on input from researchers but not participants [5]. As we continue to appreciate the shortcomings of often exclusionary, possibly exploitative, and paternalistic approaches to research, more inclusive, participant-driven approaches to data governance are needed [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Young people’s data governance preferences for their mental health data: MindKind Study findings from India, South Africa, and the United Kingdom

et al. 2023

View full text Add to dashboard Cite

Mobile devices offer a scalable opportunity to collect longitudinal data that facilitate advances in mental health treatment to address the burden of mental health conditions in young people. Sharing these data with the research community is critical to gaining maximal value from rich data of this nature. However, the highly personal nature of the data necessitates understanding the conditions under which young people are willing to share them. To answer this question, we developed the MindKind Study, a multinational, mixed methods study that solicits young people’s preferences for how their data are governed and quantifies potential participants’ willingness to join under different conditions. We employed a community-based participatory approach, involving young people as stakeholders and co-researchers. At sites in India, South Africa, and the UK, we enrolled 3575 participants ages 16–24 in the mobile app-mediated quantitative study and 143 participants in the public deliberation-based qualitative study. We found that while youth participants have strong preferences for data governance, these preferences did not translate into (un)willingness to join the smartphone-based study. Participants grappled with the risks and benefits of participation as well as their desire that the “right people” access their data. Throughout the study, we recognized young people’s commitment to finding solutions and co-producing research architectures to allow for more open sharing of mental health data to accelerate and derive maximal benefit from research.

show abstract

Practical Data Stewardship

Cited by 3 publications

References 0 publications

Un-Paradoxing Privacy: Considering Hopeful Trust

Un-Paradoxing Privacy: Considering Hopeful Trust

Defining acceptable data collection and reuse standards for queer artificial intelligence research in mental health: protocol for the online PARQAIR-MH Delphi study

Young people’s data governance preferences for their mental health data: MindKind Study findings from India, South Africa, and the United Kingdom

Contact Info

Product

Resources

About