Procedural Fairness Through Decoupling Objectionable Data Generating Components
This addresses fairness issues in AI systems for affected individuals, though it is incremental as it builds on existing procedural fairness concepts.
The paper tackles the problem of disguised procedural unfairness in automated decision-making, where neutral aspects of data generation are inadvertently altered, and proposes a framework to decouple objectionable components from neutral ones using reference points and value instantiation rules.
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.