Procedural Fairness Through Decoupling Objectionable Data Generating Components
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Abstract (arXiv)
We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (i.e., not problematic) aspects of data generating process, and/or the lack of procedural assurance of the greatest benefit of the least advantaged individuals. Inspired by John Rawls's advocacy for pure procedural justice, we view automated decision-making as a microcosm of social institutions, and consider how the data generating process itself can satisfy the requirements of procedural fairness. We propose a framework that decouples the objectionable data generating components from the neutral ones by utilizing reference points and the associated value instantiation rule. Our findings highlight the necessity of preventing disguised procedural unfairness, drawing attention not only to the objectionable data generating components that we aim to mitigate, but also more importantly, to the neutral components that we intend to keep unaffected.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 53 of its 99 references that are in the map. First posted on arXiv 5 Nov 2023.
Builds on
53 of 99 references.
References · 53
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38 more
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