A Distributionally Robust Approach to Fair Classification
A Distributionally Robust Approach to Fair Classification
复制标题
DOI：
--
复制
发表时间：
2020-07
期刊：
影响因子：
--
通讯作者：
Bahar Taşkesen;Viet Anh Nguyen;D. Kuhn;J. Blanchet
中科院分区：
文献类型：
--
作者：
Bahar Taşkesen;Viet Anh Nguyen;D. Kuhn;J. Blanchet
我们提出了一个分布鲁棒的逻辑回归模型与不公平的惩罚，防止歧视方面的敏感属性，如性别或种族。这个模型相当于一个易于处理的凸优化问题，如果一个Wasserstein球集中在经验分布的训练数据被用来模拟分布的不确定性，如果一个新的凸不公平措施被用来激励均衡的机会。我们证明，由此产生的分类提高公平性在合成和真实的数据集的预测精度的边际损失。我们还通过利用Wasserstein球上的最佳不确定性量化技术，推导出任何预训练分类器的不公平水平上基于线性规划的置信区间。
We propose a distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnicity. This model is equivalent to a tractable convex optimization problem if a Wasserstein ball centered at the empirical distribution on the training data is used to model distributional uncertainty and if a new convex unfairness measure is used to incentivize equalized opportunities. We demonstrate that the resulting classifier improves fairness at a marginal loss of predictive accuracy on both synthetic and real datasets. We also derive linear programming-based confidence bounds on the level of unfairness of any pre-trained classifier by leveraging techniques from optimal uncertainty quantification over Wasserstein balls.