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 an ML practitioner. 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：自动化机器学习（AutoML）领域引入了能够自动化机器学习系统开发部分环节的技术，从而加速流程并降低新手入门门槛。然而，基于机器学习模型做出的决策可能会重现、放大甚至在社会中引入不公平性，对个体或群体造成伤害。为此，研究者已开始提出联合优化公平性与预测性能的AutoML系统，以减轻与公平性相关的伤害。但公平性是一个复杂且本质跨学科的课题，仅将其视为优化问题可能产生不良副作用。本研究旨在提升AutoML系统开发者对公平感知AutoML局限性的认知，同时呼吁关注AutoML作为公平性研究工具的潜力。我们全面梳理了公平相关危害的不同产生方式及其对公平感知AutoML设计的启示，得出结论：尽管公平性无法被自动化，但公平感知AutoML可在机器学习从业者的工具箱中发挥重要作用。我们指出了该方向未来研究面临的若干开放技术挑战，并倡导构建更多以用户为中心的辅助系统，以应对公平性工作中遇到的难题。