Zhenpeng Chen, Jie M. Zhang, Max Hort, Mark Harman, Federica Sarro
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引用次数: 0
Abstract
Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedicated to conducting fairness testing of ML software, and this paper offers a comprehensive survey of existing studies in this field. We collect 100 papers and organize them based on the testing workflow (i.e., how to test) and testing components (i.e., what to test). Furthermore, we analyze the research focus, trends, and promising directions in the realm of fairness testing. We also identify widely-adopted datasets and open-source tools for fairness testing.
机器学习（ML）软件的不公平行为日益引起软件工程师的关注和担忧。为了解决这一问题，已有大量研究致力于对 ML 软件进行公平性测试，本文对该领域的现有研究进行了全面调查。我们收集了 100 篇论文，并根据测试工作流程（即如何测试）和测试组件（即测试什么）对其进行了整理。此外，我们还分析了公平性测试领域的研究重点、趋势和有前途的方向。我们还确定了广泛采用的公平性测试数据集和开源工具。
期刊介绍：
Designing and building a large, complex software system is a tremendous challenge. ACM Transactions on Software Engineering and Methodology (TOSEM) publishes papers on all aspects of that challenge: specification, design, development and maintenance. It covers tools and methodologies, languages, data structures, and algorithms. TOSEM also reports on successful efforts, noting practical lessons that can be scaled and transferred to other projects, and often looks at applications of innovative technologies. The tone is scholarly but readable; the content is worthy of study; the presentation is effective.
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