论文标题
公平性能指标
Fair Performance Metric Elicitation
论文作者
论文摘要
什么是公平的性能指标？我们考虑通过度量启发的视角选择公平度量标准，这是一个选择最能反映隐性偏好的性能指标的原则框架。指标启发的使用使从业者可以将绩效和公平指标调整为手头的任务，上下文和人口。具体而言，我们提出了一种新型策略，以引起与多个敏感组的多类分类问题有关多类分类问题的群体绩效指标，其中还包括选择预测性能和违反公平性的权衡。提出的启发策略仅需要相对的偏好反馈，并且对有限样本和反馈噪声都具有鲁棒性。
What is a fair performance metric? We consider the choice of fairness metrics through the lens of metric elicitation -- a principled framework for selecting performance metrics that best reflect implicit preferences. The use of metric elicitation enables a practitioner to tune the performance and fairness metrics to the task, context, and population at hand. Specifically, we propose a novel strategy to elicit group-fair performance metrics for multiclass classification problems with multiple sensitive groups that also includes selecting the trade-off between predictive performance and fairness violation. The proposed elicitation strategy requires only relative preference feedback and is robust to both finite sample and feedback noise.
