Just public algorithms: Mapping public engagement with the use of algorithms in UK public services

Pallett, Helen; Price, Catherine; Chilvers, Jason; Burall, Simon

Abstract

This paper proposes and models a novel approach to public engagement with the use of algorithms in public services. Algorithms pose significant risks which need to be anticipated and mitigated through democratic governance, including public engagement. We argue that as the challenge of creating responsible algorithms within a dynamic innovation system is one that will never definitively be accomplished – and as public engagement is not singular or pre-given but is always constructed through performance and in relation to other processes and events – public engagement with algorithms needs to be conducted and conceptualised as relational, systemic, and ongoing. We use a systemic mapping approach to map and analyse 77 cases of public engagement with the use of algorithms in public services in the UK 2013–2020 and synthesise the potential benefits and risks of these approaches articulated across the cases. The mapping shows there was already a diversity of public engagement on this topic in the UK by 2020, involving a wide range of different policy areas, framings of the problem, affordances of algorithms, publics, and formats of public engagement. While many of the cases anticipate benefits from the adoption of these technologies, they also raise a range of concerns which mirror much of the critical literature and highlight how algorithmic approaches may sometimes foreclose alternative options for policy delivery. The paper concludes by considering how this approach could be adopted on an ongoing basis to ensure the responsible governance of algorithms in public services, through a ‘public engagement observatory’.

Citation

Pallett, H., Price, C., Chilvers, J., & Burall, S. (2024). Just public algorithms: Mapping public engagement with the use of algorithms in UK public services. Big Data and Society, 11(1), 1-16. https://doi.org/10.1177/20539517241235867

• Journal Article Type: Article
• Acceptance Date: Jan 25, 2024
• Online Publication Date: Mar 18, 2024
• Publication Date: 2024-03
• Deposit Date: Mar 25, 2024
• Publicly Available Date: Mar 25, 2024
• Journal: Big Data and Society
• Electronic ISSN: 2053-9517
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 1
• Pages: 1-16
• DOI: https://doi.org/10.1177/20539517241235867
• Public URL: https://nottingham-repository.worktribe.com/output/33016287
• Publisher URL: https://journals.sagepub.com/doi/10.1177/20539517241235867