Fairness for Robust Log Loss Classification
Fairness for Robust Log Loss Classification
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DOI：
10.1609/aaai.v34i04.6002
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发表时间：
2019-03
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影响因子：
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通讯作者：
Ashkan Rezaei;Rizal Fathony;Omid Memarrast;Brian D. Ziebart
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作者：
Ashkan Rezaei;Rizal Fathony;Omid Memarrast;Brian D. Ziebart
开发高精度的分类方法，同时避免不同群体的不公平待遇，对于社交应用中的数据驱动决策变得越来越重要。许多现有方法通过直接形成约束优化来对所选分类器(例如，Logistic回归)实施公平性约束。取而代之的是，我们从分布稳健性的第一原则中重新推导出一个新的分类器，该分类器将公平标准合并到最坏情况的对数损失最小化中。这种构造采用极小极大博弈的形式，并产生类似于截断Logistic回归的参数指数族条件分布。我们给出了我们方法的凸性和渐近收敛方面的理论好处。然后，我们在三个基准公平性数据集上展示了我们的方法的实际优势。
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.