算法本身可具偏见，厘清概念是应对算法歧视的关键
Fuck the Algorithm: Conceptual Issues in Algorithmic Bias
- 辨析算法与偏见的多重含义，指出算法能成为偏见载体
- 实证显示招聘、执法、医疗等领域存在数据偏见
- 适合关注算法伦理、责任归属的研究者与政策制定者
算法偏见近年来引发广泛争议。为澄清核心问题并推动解决，需深入理解相关概念。本文聚焦于‘算法本身不可能有偏见’这一争议性主张，分析‘算法’的本质，并区分多种‘偏见’的语义。文章阐明道德层面的偏见如何源于统计偏见，并关联到政治实体与压迫性事物的既有理论。在招聘、执法、医疗等领域的数据偏见已被识别。算法自身被认定为偏见源头的案例包括影响媒体消费的推荐系统、改变引文模式的学术搜索引擎，以及2020年英国由算法主导的A-Level成绩评定。承认算法可具偏见，是明确责任、防范算法化歧视的前提。
原文摘要 · Abstract (English)
Algorithmic bias has been the subject of much recent controversy. To clarify what is at stake and to make progress resolving the controversy, a better understanding of the concepts involved would be helpful. The discussion here focuses on the disputed claim that algorithms themselves cannot be biased. To clarify this claim we need to know what kind of thing 'algorithms themselves' are, and to disambiguate the several meanings of 'bias' at play. This further involves showing how bias of moral import can result from statistical biases, and drawing connections to previous conceptual work about political artifacts and oppressive things. Data bias has been identified in domains like hiring, policing and medicine. Examples where algorithms themselves have been pinpointed as the locus of bias include recommender systems that influence media consumption, academic search engines that influence citation patterns, and the 2020 UK algorithmically-moderated A-level grades. Recognition that algorithms are a kind of thing that can be biased is key to making decisions about responsibility for harm, and preventing algorithmically mediated discrimination.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。