Res Publica:1-17 (forthcoming)

Authors
Sune Holm
University of Copenhagen
Abstract
With the increasing use of algorithms in high-stakes areas such as criminal justice and health has come a significant concern about the fairness of prediction-based decision procedures. In this article I argue that a prominent class of mathematically incompatible performance parity criteria can all be understood as applications of John Broome’s account of fairness as the proportional satisfaction of claims. On this interpretation these criteria do not disagree on what it means for an algorithm to be fair. Rather they express different understandings of what grounds a claim to a good being allocated by an algorithmic decision procedure. I then argue that an important implication of the Broomean interpretation is that it strengthens the case for outcome-based criteria. Finally, I consider how a version of the levelling-down objection to performance parity criteria arises within the Broomean account.
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DOI 10.1007/s11158-022-09546-3
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References found in this work BETA

On Statistical Criteria of Algorithmic Fairness.Brian Hedden - 2021 - Philosophy and Public Affairs 49 (2):209-231.
Fairness.John Broome - 1991 - Proceedings of the Aristotelian Society 91:87 - 101.

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