{"title":"补救和世界构建:住房正义的算法补偿的不同方法","authors":"Aurora Zhang","doi":"10.1177/20539517231202983","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":"53 1","pages":"0"},"PeriodicalIF":6.5000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Redress and worldmaking: Differing approaches to algorithmic reparations for housing justice\",\"authors\":\"Aurora Zhang\",\"doi\":\"10.1177/20539517231202983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":47834,\"journal\":{\"name\":\"Big Data & Society\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":6.5000,\"publicationDate\":\"2023-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Big Data & Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/20539517231202983\",\"RegionNum\":1,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SOCIAL SCIENCES, INTERDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data & Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/20539517231202983","RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOCIAL SCIENCES, INTERDISCIPLINARY","Score":null,"Total":0}
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.
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.