Pragmatic Fairness: Developing Policies with Outcome Disparity Control
This work addresses fairness in policy-making for domains like hiring or lending, but it is incremental as it builds on existing causal fairness methods.
The authors tackled the problem of designing optimal policies under fairness constraints by introducing a causal framework with two fairness constraints: moderation breaking to reduce outcome disparity and equal benefit to distribute gains equally across sensitive groups, demonstrating their methods on semi-synthetic experiments.
We introduce a causal framework for designing optimal policies that satisfy fairness constraints. We take a pragmatic approach asking what we can do with an action space available to us and only with access to historical data. We propose two different fairness constraints: a moderation breaking constraint which aims at blocking moderation paths from the action and sensitive attribute to the outcome, and by that at reducing disparity in outcome levels as much as 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.