Trust in data
Robin Steedman, Helen Kennedy, Rhianne Jones
In August 2019, we ran a citizen jury with diverse citizens to explore their trust in data-driven systems and data management models. Slides which reflect what we did during our citizen jury can be found here.
Citizen juries are a policy making tool where diverse citizens are brought together to debate a complex issue of social importance and make a policy recommendation. Citizen juries seek to facilitate participatory and deliberative democracy, in which citizens work together not simply to provide opinions on important issues, but to synthesize those opinions together and present answers to complex questions. In order to facilitate an informed discussion, experts are brought in to present evidence and a facilitator is used to ensure that issues are approached from multiple angles. In citizen juries, citizens are thus given a chance to contribute their informed opinions about issues that could materially impact their lives.
Citizen juries often extend over several days, but with limited resources available, we had to keep our workshop to one day. We incorporated key citizen jury elements of deliberation and informed decision making into our workshop. We included presentations from domain experts in data-driven systems and data management models. Expert 1 introduced the benefits and harms of data-driven systems that have been identified by other experts. Expert 2 introduced five data management models, including data trusts and personal data stores, and their advantages and disadvantages. 12 participants engaged in a facilitated discussion where they considered the benefits and weaknesses of different approaches. Participants decided as a group the most important criteria for the design of trustworthy data-driven systems and data management models.
The data-driven landscape is increasingly characterized by suspicion about what happens to the data we produce as a result of everyday digital activity, scandals involving major social media platforms, reports about discriminatory data-driven systems, misinformation and so-called fake news. In this context, trust is important, but it’s also elusive. The number of recent events and initiatives focusing on trust in data-driven systems attests to growing concern with this topic.
Trust is elusive in our current data-driven economy, and yet it is necessary for data-based systems to function in a sustainable way and with the support of the citizens on whose data it relies. In this project we will explore public understanding of future data-driven systems, perceptions of AI futures and criteria for trusted data systems using qualitative methods, in the form of focus group research.
In some research into trust in data practices, it is assumed that there is a link between trust and understanding. The doteveryone research cited above concludes that without understanding, people are not able to make informed choices about technologies and ‘it is likely that distrust of technologies may grow’. In contrast, in qualitative research into how people manage data in their daily lives, Pink et al (2018) show that trust has an affective dimension and its relationship with feeling is therefore as important as its relationship with understanding. Research in Human Data Interaction identifies legibility as a key enabler for agency and as a pillar for building trusted relationships with data driven technologies, but the relationship between understanding and feelings in producing trust remains largely unexplored. We explored the complex interaction of knowledge, feelings, and trust as they relate to current and future data practices by holding a citizen-jury-style workshop.
This research extended the survey we conducted. By deliberating with participants on future data management scenarios, we were able to access their thoughts and feelings that cannot be accessed by a survey. Comparing methods in this way, we were also interested in exploring the best methods for engaging publics in conversation about these matters, to ensure that future data driven system fit with the ethics and values acceptable to wider society.
Materials we used during the citizen jury
In the interests of transparency, we provide links below to materials we used in the citizen jury:
- Key definitions handed out to jurors at the start of the citizen jury
- Script used to structure the day
- Slides used throughout the day
What we found
Findings from the citizen jury will be published as a book chapter soon. Citation details and chapter abstract can be found below.
Kennedy, H., Steedman, R. and Jones, R. (forthcoming) Researching Public Trust in Datafication: Reflections on the Deliberative Citizen Jury as Method. In The Ambivalences of Data Power: new perspectives in critical data studies, edited by Andreas Hepp, Juliane Jarke and Leif Kramp.
How can we engage the public in issues relating to data futures when these matters are often complex, opaque and difficult to understand? Answers to this question are urgently needed given mounting concern about the potential negative consequences of concentrated corporate data power. The citizen jury offers one solution. Citizen juries bring diverse citizens together to debate a complex issue of social importance and make a policy recommendation. In this chapter, we reflect on a citizen jury experiment where participants discussed their criteria for the design of ethical, just and trustworthy data-driven systems. We argue that the synthesis of participants’ opinions resulting from the deliberative approach is a unique strength of the citizen jury as a method for researching public perceptions of data power. However, we also argue that the expertise provided by experts that informs deliberation shapes the process and the conclusions that citizen jurors draw. It is well recognised in the social sciences that all empirical research findings are shaped by their methods, yet this is not widely acknowledged in research into public perceptions of datafication or citizen juries. We end the chapter with a call for greater critical thinking about methods in relation to both of these areas.