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Poster
Testing Group Fairness via Optimal Transport Projections
Nian Si · Karthyek Murthy · Jose Blanchet · Viet Anh Nguyen

Thu Jul 22 09:00 PM -- 11:00 PM (PDT) @ None #None
in Poster Session 6 »

We have developed a statistical testing framework to detect if a given machine learning classifier fails to satisfy a wide range of group fairness notions. Our test is a flexible, interpretable, and statistically rigorous tool for auditing whether exhibited biases are intrinsic to the algorithm or simply due to the randomness in the data. The statistical challenges, which may arise from multiple impact criteria that define group fairness and which are discontinuous on model parameters, are conveniently tackled by projecting the empirical measure to the set of group-fair probability models using optimal transport. This statistic is efficiently computed using linear programming, and its asymptotic distribution is explicitly obtained. The proposed framework can also be used to test for composite fairness hypotheses and fairness with multiple sensitive attributes. The optimal transport testing formulation improves interpretability by characterizing the minimal covariate perturbations that eliminate the bias observed in the audit.

Keywords: Regularization · Fairness, Accountability, and Transparency · Theory · Algorithms -> Sparsity and Compressed Sensing; Optimization; Theory · Social Aspects of Machine Learning

Author Information

Nian Si (Stanford University)
Karthyek Murthy ( Singapore University of Technology and Design)
Jose Blanchet (Stanford University)
Viet Anh Nguyen (Stanford University / VinAI Research)

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