论文标题
机器学习中的准确性和公平性权衡：一种随机的多目标方法
Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic Multi-Objective Approach
论文作者
论文摘要
在将机器学习应用于现实生活中的决策系统中，例如信用评分和刑事司法，预测结果可能会歧视具有敏感属性的人，从而导致不公平。公平机器学习中常用的策略是在最小化预测损失中将公平作为约束或惩罚术语包括在内，这最终限制了提供给决策者的信息。在本文中，我们通过制定一个随机的多目标优化问题来介绍一种新方法来处理公平性，相应的帕累托（Pareto）唯一，全面地定义了准确性的权衡。然后，我们应用了一种随机近似类型的方法来有效地获得良好的帕累托前部，这样我们就可以处理以流方式到达的训练数据。
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.
