Authors
Annette Zimmermann
Princeton University
Chad Lee-Stronach
Stanford University
Abstract
It is becoming more common that the decision-makers in private and public institutions are predictive algorithmic systems, not humans. This article argues that relying on algorithmic systems is procedurally unjust in contexts involving background conditions of structural injustice. Under such nonideal conditions, algorithmic systems, if left to their own devices, cannot meet a necessary condition of procedural justice, because they fail to provide a sufficiently nuanced model of which cases count as relevantly similar. Resolving this problem requires deliberative capacities uniquely available to human agents. After exploring the limitations of existing formal algorithmic fairness strategies, the article argues that procedural justice requires that human agents relying wholly or in part on algorithmic systems proceed with caution: by avoiding doxastic negligence about algorithmic outputs, by exercising deliberative capacities when making similarity judgments, and by suspending belief and gathering additional information in light of higher-order uncertainty.
Keywords ethics of artificial intelligence  procedural fairness  structural injustice  algorithmic injustice  like cases maxim  higher-order uncertainty  intersectionality  causal interpretations of fairness  epistemic duties  doxastic negligence
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What is a (Social) Structural Explanation?Sally Haslanger - 2016 - Philosophical Studies 173 (1):113-130.
Varieties of Moral Encroachment.Renée Jorgensen Bolinger - 2020 - Philosophical Perspectives 34 (1):5-26.

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