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.

翻译：我们揭示并解决了伪装性程序不公平这一常被忽视的重要问题，即对数据生成过程中中性（即无问题）组件行为的潜在无意改变，以及/或缺乏对最不利个体获得最大利益的程序性保障。受约翰·罗尔斯纯粹程序正义理论的启发，我们将自动化决策视为社会制度的微观缩影，探讨数据生成过程本身如何满足程序公平的要求。我们提出一个框架，通过利用参考点及其关联的价值实例化规则，将有异议的数据生成组件与中性组件解耦。研究结果强调了防止伪装性程序不公平的必要性，不仅需关注我们旨在缓解的有异议数据生成组件，更关键的是要保护那些应保持不受影响的中性组件。