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We reveal and address the frequently overlooked yet important issue ofdisguised procedural unfairness, namely, the potentially inadvertentalterations on the behavior of neutral (i.e., not problematic) aspects of datagenerating process, and/or the lack of procedural assurance of the greatestbenefit of the least advantaged individuals. Inspired by John Rawls's advocacyfor pure procedural justice, we view automated decision-making as a microcosmof social institutions, and consider how the data generating process itself cansatisfy the requirements of procedural fairness. We propose a framework thatdecouples the objectionable data generating components from the neutral ones byutilizing reference points and the associated value instantiation rule. Ourfindings highlight the necessity of preventing disguised procedural unfairness,drawing attention not only to the objectionable data generating components thatwe aim to mitigate, but also more importantly, to the neutral components thatwe intend to keep unaffected.

Procedural Fairness Through Decoupling Objectionable Data Generating Components