AutoML in The Wild: Obstacles, Workarounds, and Expectations
It addresses the problem of understanding user adoption and limitations of AutoML for practitioners and developers, but is incremental as it builds on prior human-in-the-loop research.
This study investigated how users adopt AutoML in real-world settings, revealing that they actively overcome challenges related to customizability, transparency, and privacy through workarounds and make cautious decisions on a case-by-case basis.
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.