Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
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探索机器学习从业者（尝试）如何使用公平工具包
DOI：
10.1145/3531146.3533113
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Zhu, Haiyi
中科院分区：
文献类型：
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作者：
Deng, Wesley Hanwen;Nagireddy, Manish;Lee, Michelle Seng;Singh, Jatinder;Wu, Zhiwei Steven;Holstein, Kenneth;Zhu, Haiyi
近年来，我们看到了许多开源机器学习公平性工具包的发展，旨在帮助机器学习从业者评估和解决他们系统中的不公平性。然而，关于机器学习从业者如何在实践中实际使用这些工具包的研究很少。在本文中，我们对行业从业者如何（试图）使用现有的公平工具包进行了首次深入的实证探索。特别是，我们进行了有声思考访谈，以了解参与者如何学习和使用公平工具包，并通过匿名在线调查探索了我们发现的普遍性。我们确定了几个公平工具包的机会，以更好地解决从业者的需求，并在有效和负责任地使用工具包时为他们提供支持。基于这些发现，我们强调了对未来开源公平性工具包设计的影响，这些工具包可以支持从业者更好地围绕机器学习公平性工作进行情境化、沟通和协作。
Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems. However, there has been little research investigating how ML practitioners actually use these toolkits in practice. In this paper, we conducted the first in-depth empirical exploration of how industry practitioners (try to) work with existing fairness toolkits. In particular, we conducted think-aloud interviews to understand how participants learn about and use fairness toolkits, and explored the generality of our findings through an anonymous online survey. We identified several opportunities for fairness toolkits to better address practitioner needs and scaffold them in using toolkits effectively and responsibly. Based on these findings, we highlight implications for the design of future open-source fairness toolkits that can support practitioners in better contextualizing, communicating, and collaborating around ML fairness efforts.