Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches
Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches
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DOI：
10.1613/jair.1.13196
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Lindsay Weinberg
中科院分区：
文献类型：
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作者：
Lindsay Weinberg
这篇调查文章评估和比较了目前对机器学习(ML)中促进公平的技术干预的现有批评，这些批评来自一系列非计算学科，包括哲学、女权主义研究、批评种族和伦理研究、法律研究、人类学和科学技术研究。它弥合了认知上的分歧，以便提供一个跨学科的理解，即霸权计算方法对ML公平的可能性和局限性，为社会最边缘的人产生公正的结果。这篇文章是根据9个主要的批评主题组织的，其中这些不同的领域相交：1)人工智能公平研究中的“公平”是如何定义的；2)人工智能系统要解决的问题是如何形成的；3)抽象对人工智能工具如何发挥作用及其导致技术解决方案的倾向的影响；4)种族分类如何在人工智能公平研究中运行；5)使用人工智能公平措施来避免监管和进行伦理清洗；6)在人工智能公平考虑中缺乏参与性设计和民主审议；7)数据收集做法根深蒂固的“偏见”，是非共识的，缺乏透明度；8)掠夺性地将边缘化群体纳入人工智能系统；以及9)缺乏对人工智能长期社会和伦理结果的参与。根据这些批评，文章最后设想了未来ML公平的研究方向，积极扰乱社会中根深蒂固的权力动态和结构性不公正。
This survey article assesses and compares existing critiques of current fairness-enhancing technical interventions in machine learning (ML) that draw from a range of non-computing disciplines, including philosophy, feminist studies, critical race and ethnic studies, legal studies, anthropology, and science and technology studies. It bridges epistemic divides in order to offer an interdisciplinary understanding of the possibilities and limits of hegemonic computational approaches to ML fairness for producing just outcomes for society’s most marginalized. The article is organized according to nine major themes of critique wherein these different fields intersect: 1) how "fairness" in AI fairness research gets defined; 2) how problems for AI systems to address get formulated; 3) the impacts of abstraction on how AI tools function and its propensity to lead to technological solutionism; 4) how racial classification operates within AI fairness research; 5) the use of AI fairness measures to avoid regulation and engage in ethics washing; 6) an absence of participatory design and democratic deliberation in AI fairness considerations; 7) data collection practices that entrench “bias,” are non-consensual, and lack transparency; 8) the predatory inclusion of marginalized groups into AI systems; and 9) a lack of engagement with AI’s long-term social and ethical outcomes. Drawing from these critiques, the article concludes by imagining future ML fairness research directions that actively disrupt entrenched power dynamics and structural injustices in society.