Gultchin, Limor; Guo, Siyuan; Malek, Alan; Chiappa, Silvia; Silva, Ricardo; (2024) Pragmatic Fairness: Developing Policies with Outcome Disparity Control. In: Locatello, Francesco and Didelez, Vanessa, (eds.) Proceedings of Machine Learning Research. (pp. pp. 243-264). PMLR: Los Angeles, California, USA.

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Abstract

We introduce a causal framework for designing optimal policies that satisfy classes of fairness constraints. We take a pragmatic approach asking what we can do with an action space available from historical data, with no further experimentation and novel actions immediately available. We propose two different fairness constraints: a “moderation breaking’ constraint which aims at reducing disparity in outcome levels across sensitive attributes to the extent the provided action space permits; and an “equal benefit” constraint which aims at distributing gain from the new and maximized policy equally across sensitive attribute levels, and thus at keeping pre-existing preferential treatment in place or avoiding the introduction of new disparity. We introduce practical methods for implementing the constraints and illustrate their uses on experiments with semi-synthetic models.

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