Poster
in
Workshop: Responsible Decision Making in Dynamic Environments

Long Term Fairness for Minority Groups via Performative Distributionally Robust Optimization

Liam Peet-Pare ⋅ Alona Fyshe ⋅ Nidhi Hegde

Abstract

Fairness researchers in machine learning (ML) have coalesced around several fairness criteria which provide formal definitions of what it means for an ML model to be fair. However, these criteria have some serious limitations. We identify four key shortcomings of these formal fairness criteria, and aim to help to address them by extending performative prediction to include a distributionally robust objective.

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