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.

翻译：公平分类的目标是学习一个不因种族和性别等敏感属性而歧视个人群体的分类师。设计公平算法的一个办法就是将公平概念的放松作为正规化的条件或限制优化的问题。我们观察到,双曲正切功能可以接近指标功能。我们利用这一属性来定义一种与公平概念相近的不同放松,比现有的放松要好得多。此外,我们提议了一个模型-不可知的多目标结构,可以同时优化多重公平概念和多重敏感属性,并支持所有基于统计均等的公平概念。我们利用我们与多目标结构的放松来学习公平的分类师。对公共数据集的实验表明,我们的方法在准确性方面的损失远远低于目前与不受限制的模式相比的扭曲性算法。