AutoML in The Wild: Obstacles, Workarounds, and Expectations
AutoML in The Wild: Obstacles, Workarounds, and Expectations
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DOI：
10.1145/3544548.3581082
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发表时间：
2023-02
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影响因子：
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通讯作者：
Yuan Sun;Qiurong Song;Xinning Gui;Fenglong Ma;Ting Wang
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文献类型：
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作者：
Yuan Sun;Qiurong Song;Xinning Gui;Fenglong Ma;Ting Wang
自动化机器学习（AutoML）旨在使普通用户能够使用机器学习技术。最近的工作研究了人类在整个标准ML工作流中增强AutoML功能方面的作用。然而，从整体的角度理解用户如何在复杂的现实环境中采用现有的AutoML解决方案也很重要。为了填补这一空白，本研究对AutoML用户（N = 19）进行了半结构化访谈，重点了解(1)用户在实际实践中遇到的AutoML限制，(2)用户应对这些限制的策略，以及(3)这些限制和变通方法如何影响他们对AutoML的使用。我们的研究结果表明，用户积极行使用户代理来克服三个主要挑战，即可定制性、透明度和隐私。此外，用户会根据具体情况谨慎地决定是否以及如何应用AutoML。最后，我们推导了开发未来AutoML解决方案的设计含义。
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.