TY - GEN
T1 - AutoML in The Wild
T2 - 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023
AU - Sun, Yuan
AU - Song, Qiurong
AU - Gui, Xinning
AU - Ma, Fenglong
AU - Wang, Ting
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/4/19
Y1 - 2023/4/19
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85160009888&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85160009888&partnerID=8YFLogxK
U2 - 10.1145/3544548.3581082
DO - 10.1145/3544548.3581082
M3 - Conference contribution
AN - SCOPUS:85160009888
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 23 April 2023 through 28 April 2023
ER -