In the application of machine learning to real-life decision-making systems, e.g., credit scoring and criminal justice, the prediction outcomes might discriminate against people with sensitive attributes, leading to unfairness. The commonly used strategy in fair machine learning is to include fairness as a constraint or a penalization term in the minimization of the prediction loss, which ultimately limits the information given to decision-makers. In this paper, we introduce a new approach to handle fairness by formulating a stochastic multi-objective optimization problem for which the corresponding Pareto fronts uniquely and comprehensively define the accuracy-fairness trade-offs. We have then applied a stochastic approximation-type method to efficiently obtain well-spread and accurate Pareto fronts, and by doing so we can handle training data arriving in a streaming way.

翻译：在将机器学习应用到现实决策系统(例如信用评分和刑事司法)时,预测结果可能会歧视具有敏感属性的人,导致不公平;公平机器学习中常用的战略是将公平作为限制或惩罚性术语纳入预测损失的最小化,从而最终限制向决策者提供的信息;在本文件中,我们引入了一种处理公平问题的新方法,即提出一个随机多目标优化问题,对此,相应的Pareto阵线将独特和全面地界定准确性与公平性之间的取舍。我们随后采用了一种随机近似型方法,以便有效地获得广度和准确的Pareto战线,通过这样做,我们可以处理以流传方式到达的数据的培训工作。