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Poster
Achieving Equalized Odds by Resampling Sensitive Attributes
Yaniv Romano · Stephen Bates · Emmanuel Candes

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #1077

We present a flexible framework for learning predictive models that approximately satisfy the equalized odds notion of fairness. This is achieved by introducing a general discrepancy functional that rigorously quantifies violations of this criterion. This differentiable functional is used as a penalty driving the model parameters towards equalized odds. To rigorously evaluate fitted models, we develop a formal hypothesis test to detect whether a prediction rule violates this property, the first such test in the literature. Both the model fitting and hypothesis testing leverage a resampled version of the sensitive attribute obeying equalized odds, by construction. We demonstrate the applicability and validity of the proposed framework both in regression and multi-class classification problems, reporting improved performance over state-of-the-art methods. Lastly, we show how to incorporate techniques for equitable uncertainty quantification---unbiased for each group under study---to communicate the results of the data analysis in exact terms.

Author Information

Yaniv Romano (Stanford University)
Stephen Bates (UC Berkeley)
Emmanuel Candes (Stanford University)

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