Public Justification and Normatively Meaningful Bias: Against Imposing Egalitarian Accounts of Algorithmic Bias

In a number of policy, institutional, activist and advocacy contexts, attributing bias to an algorithm does not just describe the algorithm but also imposes a particular, normatively laden conception of bias on others. Given the normative content of such bias attributions, this would involve making moral demands on others to rectify the algorithm, compensate the victims of such bias and/or not unselectively deploy the algorithm. It is also the case that moral demands, especially in the above-mentioned contexts, are subject to a public justification requirement. As it turns out, the dominant accounts of bias in the literature presuppose some version of egalitarianism about justice and that any action that causally contributes to an unjust situation is itself wrong. Since these presuppositions are subject to reasonable disagreement, bias attributions in such situations are wrong because they violate the public justification requirement. In response, we develop a publicly justifiable conception of algorithmic bias.

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Publication Details

Journal
Bioethics
Published
2025-11-14
DOI
https://doi.org/10.1111/bioe.70047
Citations
1
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
2.37

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article

Public Justification and Normatively Meaningful Bias: Against Imposing Egalitarian Accounts of Algorithmic Bias

Julian Savulescu, Anantharaman Muralidharan, G. Owen Schaefer
1 citations
Bioethics
Ethics and Social Impacts of AI
2.37
article

Public Justification and Normatively Meaningful Bias: Against Imposing Egalitarian Accounts of Algorithmic Bias

Julian Savulescu, Anantharaman Muralidharan, G. Owen Schaefer
article en
1 citations

Abstract

In a number of policy, institutional, activist and advocacy contexts, attributing bias to an algorithm does not just describe the algorithm but also imposes a particular, normatively laden conception of bias on others. Given the normative content of such bias attributions, this would involve making moral demands on others to rectify the algorithm, compensate the victims of such bias and/or not unselectively deploy the algorithm. It is also the case that moral demands, especially in the above-mentioned contexts, are subject to a public justification requirement. As it turns out, the dominant accounts of bias in the literature presuppose some version of egalitarianism about justice and that any action that causally contributes to an unjust situation is itself wrong. Since these presuppositions are subject to reasonable disagreement, bias attributions in such situations are wrong because they violate the public justification requirement. In response, we develop a publicly justifiable conception of algorithmic bias.

BioethicsVol. 40(1)
National University of Singapore (SG), University of Oxford (GB), Murdoch Children's Research Institute (AU)
Wellcome Trust, Children’s Hospital of Wisconsin Research Institute, Murdoch Children's Research Institute, National Research Foundation, National Research Foundation Singapore, National University Health System, Medical Research Council, National Medical Research Council
Openalex Percentile: Top 8%
Ethics and Social Impacts of AI
2.37
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