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  • 5 - Lost in Translation: The Troubling Logics Underpinning the Embrace of AI-driven Prediction Tools by Public Authorities for ‘Citizen Scoring’

  • from Part II - Rationalities, Styles and Logics
  • Edited by Fleur Johns, University of Sydney, Gavin Sullivan, University of Edinburgh, Dimitri Van Den Meerssche, Queen Mary University of London
  • Book:
    Global Governance by Data
    Published online:
    11 August 2026
    Print publication:
    19 November 2026, pp 142-173
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  • Summary

    Machine learning (ML) applications referred to as ‘predictive’ or ‘big data’ analytics, now ubiquitous in retail, entertainment and logistics, are becoming increasingly common in public sector contexts. This chapter asserts that the embrace of data-driven ‘citizen scoring’ systems which claim to estimate an individual’s ‘risk’ of specific behaviours is underpinned by a set of promises, assumptions, beliefs and rationalities (collectively called ‘logics’). I critique three specific claims that have encouraged the adoption of commercial ML techniques by the state: (a) that ML produces more accurate predictions, (b) that these predictions offer valuable ‘actionable insight’ for public authorities, and (c) that ‘early intervention’ based on such insight is desirable. Thanks to the uncritical adoption of commercial ML methods in the public sector, power and authority are being illegitimately, and sometimes unlawfully, redistributed in ways that produce injustice, to the detriment of some of the most vulnerable members of society.