TY - JOUR AU - Rezaei, Ashkan AU - Fathony, Rizal AU - Memarrast, Omid AU - Ziebart, Brian PY - 2020/04/03 Y2 - 2024/03/29 TI - Fairness for Robust Log Loss Classification JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 04 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v34i04.6002 UR - https://ojs.aaai.org/index.php/AAAI/article/view/6002 SP - 5511-5518 AB - <p>Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications. Many existing methods enforce fairness constraints on a selected classifier (e.g., logistic regression) by directly forming constrained optimizations. We instead re-derive a new classifier from the first principles of distributional robustness that incorporates fairness criteria into a worst-case logarithmic loss minimization. This construction takes the form of a minimax game and produces a parametric exponential family conditional distribution that resembles truncated logistic regression. We present the theoretical benefits of our approach in terms of its convexity and asymptotic convergence. We then demonstrate the practical advantages of our approach on three benchmark fairness datasets.</p> ER -