论文标题
通过模型不合时宜的多目标算法来解决分类中的公平性
Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithm
论文作者
论文摘要
分类公平的目的是学习一个分类器，该分类器不会根据敏感属性（例如种族和性别）来区分个人群体。设计公平算法的一种方法是将公平概念的放松作为正规化术语或在受限的优化问题中。我们观察到双曲线切线函数可以近似指示器函数。我们利用这一属性来定义一个可区分的放松，该放松近似于公平的概念，证明比现有的放松更好。此外，我们还提出了一个模型的多目标体系结构，可以同时优化多个公平概念和多个敏感属性，并支持所有基于统计的公平概念。我们将放松与多目标体系结构一起学习公平的分类器。公共数据集上的实验表明，相对于当前的偏差算法，相对于不受约束的模型，我们的方法的准确性损失明显低得多。
The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One approach to designing fair algorithms is to use relaxations of fairness notions as regularization terms or in a constrained optimization problem. We observe that the hyperbolic tangent function can approximate the indicator function. We leverage this property to define a differentiable relaxation that approximates fairness notions provably better than existing relaxations. In addition, we propose a model-agnostic multi-objective architecture that can simultaneously optimize for multiple fairness notions and multiple sensitive attributes and supports all statistical parity-based notions of fairness. We use our relaxation with the multi-objective architecture to learn fair classifiers. Experiments on public datasets show that our method suffers a significantly lower loss of accuracy than current debiasing algorithms relative to the unconstrained model.
