论文标题
通过表演分布强大的优化，少数群体的长期公平性
Long Term Fairness for Minority Groups via Performative Distributionally Robust Optimization
论文作者
论文摘要
机器学习中的公平研究人员（ML）围绕几个公平标准结合，这些标准为ML模型公平提供了正式的定义。但是，这些标准有一些严重的局限性。我们确定了这些正式公平标准的四个主要缺点，并旨在通过扩展性能预测以包含分配稳健的目标来帮助解决这些问题。
Fairness researchers in machine learning (ML) have coalesced around several fairness criteria which provide formal definitions of what it means for an ML model to be fair. However, these criteria have some serious limitations. We identify four key shortcomings of these formal fairness criteria, and aim to help to address them by extending performative prediction to include a distributionally robust objective.
