{"title":"风险评估的虚假承诺:认识改革和公平的限制","authors":"Ben Green","doi":"10.1145/3351095.3372869","DOIUrl":null,"url":null,"abstract":"Risk assessments have proliferated in the United States criminal justice system. The theory of change motivating their adoption involves two key assumptions: first, that risk assessments will reduce human biases by making objective decisions, and second, that risk assessments will promote criminal justice reform. In this paper I interrogate both of these assumptions, concluding that risk assessments are an ill-advised tool for challenging the centrality and legitimacy of incarceration within the criminal justice system. First, risk assessments fail to provide objectivity, as their use creates numerous sites of discretion. Second, risk assessments provide no guarantee of reducing incarceration; instead, they risk legitimizing the criminal justice system's structural racism. I then consider, via an \"epistemic reform,\" the path forward for criminal justice reform. I reinterpret recent results regarding the \"impossibility of fairness\" as not simply a tension between mathematical metrics but as evidence of a deeper tension between notions of equality. This expanded frame challenges the formalist, colorblind proceduralism at the heart of the criminal justice system and suggests a more structural approach to reform. Together, this analysis highlights how algorithmic fairness narrows the scope of judgments about justice and how \"fair\" algorithms can reinforce discrimination.","PeriodicalId":377829,"journal":{"name":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","volume":"214 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"59","resultStr":"{\"title\":\"The false promise of risk assessments: epistemic reform and the limits of fairness\",\"authors\":\"Ben Green\",\"doi\":\"10.1145/3351095.3372869\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Risk assessments have proliferated in the United States criminal justice system. The theory of change motivating their adoption involves two key assumptions: first, that risk assessments will reduce human biases by making objective decisions, and second, that risk assessments will promote criminal justice reform. In this paper I interrogate both of these assumptions, concluding that risk assessments are an ill-advised tool for challenging the centrality and legitimacy of incarceration within the criminal justice system. First, risk assessments fail to provide objectivity, as their use creates numerous sites of discretion. Second, risk assessments provide no guarantee of reducing incarceration; instead, they risk legitimizing the criminal justice system's structural racism. I then consider, via an \\\"epistemic reform,\\\" the path forward for criminal justice reform. I reinterpret recent results regarding the \\\"impossibility of fairness\\\" as not simply a tension between mathematical metrics but as evidence of a deeper tension between notions of equality. This expanded frame challenges the formalist, colorblind proceduralism at the heart of the criminal justice system and suggests a more structural approach to reform. Together, this analysis highlights how algorithmic fairness narrows the scope of judgments about justice and how \\\"fair\\\" algorithms can reinforce discrimination.\",\"PeriodicalId\":377829,\"journal\":{\"name\":\"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency\",\"volume\":\"214 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"59\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3351095.3372869\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3351095.3372869","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The false promise of risk assessments: epistemic reform and the limits of fairness
Risk assessments have proliferated in the United States criminal justice system. The theory of change motivating their adoption involves two key assumptions: first, that risk assessments will reduce human biases by making objective decisions, and second, that risk assessments will promote criminal justice reform. In this paper I interrogate both of these assumptions, concluding that risk assessments are an ill-advised tool for challenging the centrality and legitimacy of incarceration within the criminal justice system. First, risk assessments fail to provide objectivity, as their use creates numerous sites of discretion. Second, risk assessments provide no guarantee of reducing incarceration; instead, they risk legitimizing the criminal justice system's structural racism. I then consider, via an "epistemic reform," the path forward for criminal justice reform. I reinterpret recent results regarding the "impossibility of fairness" as not simply a tension between mathematical metrics but as evidence of a deeper tension between notions of equality. This expanded frame challenges the formalist, colorblind proceduralism at the heart of the criminal justice system and suggests a more structural approach to reform. Together, this analysis highlights how algorithmic fairness narrows the scope of judgments about justice and how "fair" algorithms can reinforce discrimination.