  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.
