Fairness for Robust Log Loss Classification

Understand

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.