  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.
