Improving the fairness of machine learning models is a nuanced task that requires decision makers to reason about multiple, conflicting criteria. The majority of fair machine learning methods transform the error-fairness trade-off into a single objective problem with a parameter controlling the relative importance of error versus fairness. We propose instead to directly optimize the error-fairness tradeoff by using multi-objective optimization. We present a flexible framework for defining the fair machine learning task as a weighted classification problem with multiple cost functions. This framework is agnostic to the underlying prediction model as well as the metrics. We use multiobjective optimization to define the sample weights used in model training for a given machine learner, and adapt the weights to optimize multiple metrics of fairness and accuracy across a set of tasks. To reduce the number of optimized parameters, and to constrain their complexity with respect to population subgroups, we propose a novel meta-model approach that learns to map protected attributes to sample weights, rather than optimizing those weights directly. On a set of real-world problems, this approach outperforms current state-of-the-art methods by finding solution sets with preferable error/fairness trade-offs.

翻译：提升机器学习模型的公平性是一项需要决策者权衡多个相互冲突目标的微妙任务。大多数公平机器学习方法将误差-公平性权衡转化为单目标问题，并通过一个参数控制误差与公平性的相对重要性。我们提出直接利用多目标优化来优化误差-公平性权衡。本文提出一个灵活框架，将公平机器学习任务定义为具有多个代价函数的加权分类问题。该框架与底层预测模型及评估指标无关。我们利用多目标优化确定特定机器学习模型训练中使用的样本权重，并自适应调整权重以优化一组任务中多个公平性与准确性指标。为减少优化参数数量并约束其相对于群体子集的复杂度，我们提出一种新颖的元模型方法，该方法学习将受保护属性映射到样本权重，而非直接优化这些权重。在一组真实世界问题上，该方法通过找到具有更优误差/公平性权衡的解集，超越了当前最先进方法。