Machine learning (ML) algorithms are increasingly deployed to make critical decisions in socioeconomic applications such as finance, criminal justice, and autonomous driving. However, due to their data-driven and pattern-seeking nature, ML algorithms may develop decision logic that disproportionately distributes opportunities, benefits, resources, or information among different population groups, potentially harming marginalized communities. In response to such fairness concerns, the software engineering and ML communities have made significant efforts to establish the best practices for creating fair ML software. These include fairness interventions for training ML models, such as including sensitive features, selecting non-sensitive attributes, and applying bias mitigators. But how reliably can software professionals tasked with developing data-driven systems depend on these recommendations? And how well do these practices generalize in the presence of faulty labels, missing data, or distribution shifts? These questions form the core theme of this paper.

翻译：机器学习（ML）算法正越来越多地被部署于金融、刑事司法和自动驾驶等社会经济应用的关键决策中。然而，由于其数据驱动和模式寻求的特性，ML算法可能形成在不同人口群体间不均等地分配机会、利益、资源或信息的决策逻辑，从而可能损害边缘化群体。针对此类公平性问题，软件工程和ML社区已付出巨大努力，以建立开发公平ML软件的最佳实践。这包括针对训练ML模型的公平性干预措施，例如纳入敏感特征、选择非敏感属性以及应用偏差缓解器。但是，负责开发数据驱动系统的软件专业人员能在多大程度上可靠地依赖这些建议？在存在错误标签、数据缺失或分布偏移的情况下，这些实践的泛化能力又如何？这些问题构成了本文的核心主题。