The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices. However, decisions derived from ML models can reproduce, amplify, or even introduce unfairness in our societies, causing harm to (groups of) individuals. In response, researchers have started to propose AutoML systems that jointly optimize fairness and predictive performance to mitigate fairness-related harm. However, fairness is a complex and inherently interdisciplinary subject, and solely posing it as an optimization problem can have adverse side effects. With this work, we aim to raise awareness among developers of AutoML systems about such limitations of fairness-aware AutoML, while also calling attention to the potential of AutoML as a tool for fairness research. We present a comprehensive overview of different ways in which fairness-related harm can arise and the ensuing implications for the design of fairness-aware AutoML. We conclude that while fairness cannot be automated, fairness-aware AutoML can play an important role in the toolbox of ML practitioners. We highlight several open technical challenges for future work in this direction. Additionally, we advocate for the creation of more user-centered assistive systems designed to tackle challenges encountered in fairness work

翻译：自动机器学习（AutoML）领域引入了自动化机器学习系统开发流程的技术，既加速了开发进程，也降低了新手的使用门槛。然而，基于机器学习模型做出的决策可能在社会中复制、放大甚至引入不公平现象，对（群体）个体造成损害。为此，研究人员已开始提出联合优化公平性与预测性能的AutoML系统，以缓解公平性相关损害。但公平性本质上是一个复杂且跨学科的议题，单纯将其视为优化问题可能产生不利副作用。本文旨在提升AutoML开发人员对公平感知AutoML局限性的认知，同时呼吁关注AutoML作为公平研究工具的潜力。我们全面阐述了公平性相关损害的不同产生方式，及其对设计公平感知AutoML的启示。结论指出：尽管公平性无法被自动化，公平感知AutoML仍可成为机器学习从业者工具箱中的重要组件。我们指出了该方向未来研究需解决的若干开放性技术挑战，并倡导构建以用户为中心的辅助系统，以应对公平性工作中的实际难题。