反思公平：对霸权主义ML公平方法批评的跨学科调查
Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches
摘要：
这篇调查文章评估和比较了当前对机器学习（ML）的公平性增强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，批判性种族和民族研究，法律研究，人类学和科学技术研究。它弥合了认识上的分歧，以提供对霸权计算方法的可能性和局限性的跨学科理解，以实现ML公平，为社会最边缘化的人产生公正的结果。本文根据九个主要的批评主题进行组织，其中这些不同的领域相互交叉：1）人工智能公平研究中的“公平”如何定义; 2）人工智能系统如何解决问题; 3）抽象对人工智能工具功能的影响及其导致技术解决方案的倾向; 4）种族分类如何在人工智能公平研究中运作; 5）使用人工智能公平措施来避免监管和进行道德清洗; 6）在人工智能公平考虑方面缺乏参与式设计和民主审议; 7）巩固“偏见”、非共识和缺乏透明度的数据汇集做法; 8）将边缘化群体掠夺性地纳入人工智能系统; 9）缺乏对人工智能长期社会和道德结果的参与。从这些批评中，文章最后设想了未来ML公平研究的方向，这些方向积极地破坏了社会中根深蒂固的权力动态和结构性不公正。
英文摘要：
This survey article assesses and compares existing critiques of current fairness-enhancing technical interventions into 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
