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Nonconvex Optimization for Regression with Fairness Constraints
Junpei Komiyama · Akiko Takeda · Junya Honda · Hajime Shimao

Wed Jul 11 08:40 AM -- 08:50 AM (PDT) @ A6

The unfairness of a regressor is evaluated by measuring the correlation between the estimator and the sensitive attribute (e.g., race, gender, age), and the coefficient of determination (CoD) is a natural extension of the correlation coefficient when more than one sensitive attribute exists. As is well known, there is a trade-off between fairness and accuracy of a regressor, which implies a perfectly fair optimizer does not always yield a useful prediction. Taking this into consideration, we optimize the accuracy of the estimation subject to a user-defined level of fairness. However, a fairness level as a constraint induces a nonconvexity of the feasible region, which disables the use of an off-the-shelf convex optimizer. Despite such nonconvexity, we show an exact solution is available by using tools of global optimization theory. Furthermore, we propose a nonlinear extension of the method by kernel representation. Unlike most of existing fairness-aware machine learning methods, our method allows us to deal with numeric and multiple sensitive attributes.

Author Information

Junpei Komiyama (U-Tokyo)
Akiko Takeda (The Institute of Statistical Mathematics)
Junya Honda (University of Tokyo / RIKEN)
Hajime Shimao (Purdue University)

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