Sunita Sah, David Tannenbaum, Hayley M. D. Cleary, Y. Feldman, Jack Glaser, Amy Lerman, R. MacCoun, E. Maguire, P. Slovic, Barbara Spellman, C. Spohn, Cristopher Winship
{"title":"反对有偏见的决策,促进正义和平等待遇","authors":"Sunita Sah, David Tannenbaum, Hayley M. D. Cleary, Y. Feldman, Jack Glaser, Amy Lerman, R. MacCoun, E. Maguire, P. Slovic, Barbara Spellman, C. Spohn, Cristopher Winship","doi":"10.1177/237946151600200208","DOIUrl":null,"url":null,"abstract":"This article draws on the behavioral science literature to offer empirically driven policy prescriptions that can reduce the effect of bias and ameliorate unequal treatment in policing, the criminal justice system, employment, and national security.","PeriodicalId":36971,"journal":{"name":"Behavioral Science and Policy","volume":"2 1","pages":"79 - 87"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Combating Biased Decisionmaking & Promoting Justice & Equal Treatment\",\"authors\":\"Sunita Sah, David Tannenbaum, Hayley M. D. Cleary, Y. Feldman, Jack Glaser, Amy Lerman, R. MacCoun, E. Maguire, P. Slovic, Barbara Spellman, C. Spohn, Cristopher Winship\",\"doi\":\"10.1177/237946151600200208\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article draws on the behavioral science literature to offer empirically driven policy prescriptions that can reduce the effect of bias and ameliorate unequal treatment in policing, the criminal justice system, employment, and national security.\",\"PeriodicalId\":36971,\"journal\":{\"name\":\"Behavioral Science and Policy\",\"volume\":\"2 1\",\"pages\":\"79 - 87\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Behavioral Science and Policy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/237946151600200208\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavioral Science and Policy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/237946151600200208","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Social Sciences","Score":null,"Total":0}
This article draws on the behavioral science literature to offer empirically driven policy prescriptions that can reduce the effect of bias and ameliorate unequal treatment in policing, the criminal justice system, employment, and national security.