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Long Term Fairness for Minority Groups via Performative Distributionally Robust Optimization
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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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Distributionally Robust Performative Prediction
The paper defines the distributionally robust performative optimum (DRPO), minimizes worst-case performative risk over a KL uncertainty set of distribution maps, and proves it approximates the true performative optimu...
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