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Conditional trust: Citizens’ council on data-driven media personalisation and public expectations of transparency and accountability

Wong, Yen Nee; Jones, Rhia; Das, Ranjana; Jackson, Philip

Authors

Rhia Jones

Ranjana Das

Philip Jackson



Abstract

This article presents findings from a rigorous, three-wave series of qualitative research into public expectations of data-driven media technologies, conducted in England, United Kingdom. Through a range of carefully chosen scenarios and deliberations around the risks and benefits afforded by data-driven media personalisation technologies and algorithms, we paid close attention to citizens’ voices as our multidisciplinary team sought to engage the public on what ‘good’ might look like in the context of media personalisation. We paid particular attention to risks and opportunities, examining practical use-cases and scenarios, and our three-wave councils culminated in citizens producing recommendations for practice and policy. In this article, we focus particularly on citizens’ ethical assessment, critique and improvements proposed on media personalisation methods in relation to benefits, fairness, safety, transparency and accountability. Our findings demonstrate that public expectations and trust in data-driven technologies are, fundamentally, conditional, with significant emphasis placed on transparency, inclusiveness and accessibility. Our findings also point to the context dependency of public expectations, which appears more pertinent to citizens, in hard political as opposed to entertainment spaces. Our conclusions are significant for global data-driven media personalisation environments – in terms of embedding citizens’ focus on transparency and accountability, but equally, also, we argue that strengthening research methodology, innovatively and rigorously to build in citizen voices at the very inception and core of design – must become a priority in technology development.

Journal Article Type Article
Acceptance Date Jun 30, 2023
Online Publication Date Aug 18, 2023
Publication Date 2023-07
Deposit Date Aug 25, 2023
Publicly Available Date Aug 28, 2023
Print ISSN 2053-9517
Electronic ISSN 2053-9517
Publisher SAGE Publications
Peer Reviewed Peer Reviewed
Volume 10
Issue 2
DOI https://doi.org/10.1177/20539517231184892
Keywords User experience, trust, data-driven, personalisation, algorithms, publics

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