补救和世界构建:住房正义的算法补偿的不同方法

IF 6.5 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY Big Data & Society Pub Date : 2023-07-01 DOI:10.1177/20539517231202983
Aurora Zhang
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引用次数: 0

摘要

对算法正义的修复方法为现有的基于公平的框架提供了一个令人信服的替代方案,这些框架通常不足以挑战不公正的社会等级制度的技术永久化。然而,“赔偿”的定义在哲学上是有争议的。我讨论了两个相互关联但截然不同的赔偿概念:赔偿是对过去不公正的问责和补救,赔偿是一个建设性的世界建设项目,重点关注现在和未来的正义。每一种观点都为如何实现算法补偿提供了不同的建议和启发。我将此应用于美国住房不公平的案例研究,并在此背景下对“算法赔偿”提供了三种解释:首先,我们可以对住房方面的算法歧视提起诉讼。其次,我们可以使用计算方法来计算损失,并要求纠正过去的结构性住房不公正。最后,我们可以重新利用算法方法来想象更激进的抵抗努力,将渐进式改革与未来的大规模结构变革联系起来。
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Redress and worldmaking: Differing approaches to algorithmic reparations for housing justice
A reparative approach to algorithmic justice provides a compelling alternative to existing fairness-based frameworks, which are often inadequate for challenging the technological perpetuation of unjust social hierarchies. The definition of “reparations,” however, is philosophically contested. I discuss two interrelated but distinct notions of reparations: reparations as accountability and redress for past injustice, and reparations as a constructive worldmaking project focused on present and future justice. Each of these perspectives offers different recommendations and provocations for how to implement algorithmic reparations. I apply this to a case study of housing injustice in the US and offer three interpretations of “algorithmic reparations” in context: first, we can litigate instances of algorithmic discrimination in housing. Second, we can use computational methods to compute damages and demand redress for structural housing injustice in the past. Finally, we can repurpose algorithmic methods to imagine more radical resistance efforts that connect incremental reform to large-scale structural change for the future.
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来源期刊
Big Data & Society
Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
10.90
自引率
10.60%
发文量
59
审稿时长
11 weeks
期刊介绍: Big Data & Society (BD&S) is an open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities, and computing and their intersections with the arts and natural sciences. The journal focuses on the implications of Big Data for societies and aims to connect debates about Big Data practices and their effects on various sectors such as academia, social life, industry, business, and government. BD&S considers Big Data as an emerging field of practices, not solely defined by but generative of unique data qualities such as high volume, granularity, data linking, and mining. The journal pays attention to digital content generated both online and offline, encompassing social media, search engines, closed networks (e.g., commercial or government transactions), and open networks like digital archives, open government, and crowdsourced data. Rather than providing a fixed definition of Big Data, BD&S encourages interdisciplinary inquiries, debates, and studies on various topics and themes related to Big Data practices. BD&S seeks contributions that analyze Big Data practices, involve empirical engagements and experiments with innovative methods, and reflect on the consequences of these practices for the representation, realization, and governance of societies. As a digital-only journal, BD&S's platform can accommodate multimedia formats such as complex images, dynamic visualizations, videos, and audio content. The contents of the journal encompass peer-reviewed research articles, colloquia, bookcasts, think pieces, state-of-the-art methods, and work by early career researchers.
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