Procedural Fairness Through Decoupling Objectionable Data Generating Components

Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang

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Keywords: Procedural Fairness, Decouple Objectionable Component, Reference Point, Causal Fairness, Data Generating Process, Bias Mitigation

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TL;DR: We reveal and address the frequently overlooked issue of disguised procedural unfairness, and propose a framework to decouple objectionable data generating components to achieve procedural fairness.

Abstract: 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_ (Rawls, 1971; 2001), 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.

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Primary Area: societal considerations including fairness, safety, privacy

Submission Number: 734