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.

翻译：制定高度精确的分类方法,避免对不同群体不公平对待,对于社会应用中的数据驱动决策已变得日益重要。许多现有方法通过直接形成限制优化,对选定的分类者(如后勤回归)实行公平限制。我们改用分配稳健原则,将公平标准纳入最差的对数损失最小化。这种构建以迷你马克思游戏的形式进行,并产生类似于快速后勤回归的参数指数家庭有条件分布。我们介绍了我们方法的理论优势,即其共性与零食趋同。然后我们展示了我们在三个基准公平数据集上的做法的实际优势。