Procedural Fairness Through Decoupling Objectionable Data Generating Components

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.

Authors

• Zeyu Tang 0002
• Jialu Wang
• Yang Liu 0018
• Peter Spirtes
• Kun Zhang 0001

Keywords

• Procedural Fairness
• Decouple Objectionable Component
• Reference Point
• Causal Fairness
• Data Generating Process
• Bias Mitigation