AutoML in The Wild: Obstacles, Workarounds, and Expectations

TL;DR

This study explores real-world challenges faced by AutoML users, revealing how they adapt to limitations in customizability, transparency, and privacy, and providing design insights for future AutoML development.

Contribution

It offers a holistic understanding of AutoML adoption in complex settings through user interviews, highlighting practical obstacles and coping strategies.

Findings

Users exercise agency to overcome customizability, transparency, and privacy challenges.

Users adopt case-by-case decision-making for applying AutoML.

Design implications for future AutoML solutions are proposed.

Abstract

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.