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  • 11 - Supervising Automated Decisions

  • from Part III - Synergies and Safeguards
  • Edited by Zofia Bednarz, University of Sydney, Monika Zalnieriute, University of New South Wales, Sydney
  • Book:
    <i>Money, Power, and AI</i>
    Published online:
    16 November 2023
    Print publication:
    30 November 2023, pp 205-220
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  • Summary

    There is a broad consensus that human supervision holds the key to sound automated decision-making: if a decision-making policy uses the predictive outputs of a statistical algorithm, but those outputs form only part of a decision that is made ultimately by a human actor, use of those outputs will not (per se) fall foul of the requirements for due process in public and private decision-making. Thus, the focus in academic and judicial spheres has been on making sure that humans are equipped and willing to wield this ultimate decision-making power. Yet, proprietary software obscures the reasons for any given prediction; this is true both for machine learning and deterministic algorithms. And without these reasons, the decision-maker cannot accord appropriate weight to that prediction in their reasoning process. Thus, a policy of using opaque statistical software to make decisions about how to treat others is unjustified, however involved humans are along the way.