Automated machine learning (AutoML) is envisioned to make ML techniques accessible to ordinary users. Recent work has investigated the role of humans in enhancing AutoML functionality throughout a standard ML workflow. However, it is also critical to understand how users adopt existing AutoML solutions in complex, real-world settings from a holistic perspective. To fill this gap, this study conducted semi-structured interviews of AutoML users (N=19) focusing on understanding (1) the limitations of AutoML encountered by users in their real-world practices, (2) the strategies users adopt to cope with such limitations, and (3) how the limitations and workarounds impact their use of AutoML. Our findings reveal that users actively exercise user agency to overcome three major challenges arising from customizability, transparency, and privacy. Furthermore, users make cautious decisions about whether and how to apply AutoML on a case-by-case basis. Finally, we derive design implications for developing future AutoML solutions.

翻译：自动机器学习（AutoML）旨在让普通用户能够轻松使用机器学习技术。近期研究探讨了在标准机器学习工作流中，人类如何增强AutoML的功能。然而，从全局视角理解用户在复杂现实环境中采用现有AutoML解决方案的方式同样至关重要。为填补这一研究空白，本研究对AutoML用户（N=19）进行了半结构化访谈，重点探究：（1）用户在真实实践中遇到的AutoML局限性，（2）用户应对这些局限性所采用的策略，以及（3）这些局限性与应对策略如何影响用户对AutoML的使用。研究结果表明，用户会主动行使主观能动性，以克服可定制性、透明性和隐私性三大核心挑战。此外，用户会基于具体案例谨慎决策是否以及如何应用AutoML。最后，我们为未来AutoML解决方案的开发提出了设计启示。