{"title":"简单创造不公平:公平、刻板印象和可解释性的含义","authors":"J. Kleinberg, S. Mullainathan","doi":"10.1145/3328526.3329621","DOIUrl":null,"url":null,"abstract":"Algorithms can be a powerful aid to decision-making - particularly when decisions rely, even implicitly, on predictions [7]. We are already seeing algorithms play this role in domains including hiring, education, lending, medicine, and criminal justice [2, 6, 10]. As is typical in machine learning applications, accuracy is an important measure for these tasks.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"287 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"59","resultStr":"{\"title\":\"Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability\",\"authors\":\"J. Kleinberg, S. Mullainathan\",\"doi\":\"10.1145/3328526.3329621\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Algorithms can be a powerful aid to decision-making - particularly when decisions rely, even implicitly, on predictions [7]. We are already seeing algorithms play this role in domains including hiring, education, lending, medicine, and criminal justice [2, 6, 10]. As is typical in machine learning applications, accuracy is an important measure for these tasks.\",\"PeriodicalId\":416173,\"journal\":{\"name\":\"Proceedings of the 2019 ACM Conference on Economics and Computation\",\"volume\":\"287 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"59\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 ACM Conference on Economics and Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3328526.3329621\",\"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 2019 ACM Conference on Economics and Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3328526.3329621","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability
Algorithms can be a powerful aid to decision-making - particularly when decisions rely, even implicitly, on predictions [7]. We are already seeing algorithms play this role in domains including hiring, education, lending, medicine, and criminal justice [2, 6, 10]. As is typical in machine learning applications, accuracy is an important measure for these tasks.