{"title":"特征哈希对公平分类的影响","authors":"Ritik Dutta, Varun Gohil, Atishay Jain","doi":"10.1145/3371158.3371230","DOIUrl":null,"url":null,"abstract":"Learning new representations of data to reduce correlation with sensitive attributes is one method to tackle algorithmic bias. In this paper, we explore the possibility of using feature hashing as a method for learning new representations of data for fair classification. Using Difference of Equal Odds as our metric to measure fairness, we observe that using feature hashing on the Adult Dataset leads to 5.4x improvement in metric score while losing an accuracy of 6.1% compared to when the data is used as is.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Effect of Feature Hashing on Fair Classification\",\"authors\":\"Ritik Dutta, Varun Gohil, Atishay Jain\",\"doi\":\"10.1145/3371158.3371230\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Learning new representations of data to reduce correlation with sensitive attributes is one method to tackle algorithmic bias. In this paper, we explore the possibility of using feature hashing as a method for learning new representations of data for fair classification. Using Difference of Equal Odds as our metric to measure fairness, we observe that using feature hashing on the Adult Dataset leads to 5.4x improvement in metric score while losing an accuracy of 6.1% compared to when the data is used as is.\",\"PeriodicalId\":360747,\"journal\":{\"name\":\"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3371158.3371230\",\"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 7th ACM IKDD CoDS and 25th COMAD","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3371158.3371230","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Learning new representations of data to reduce correlation with sensitive attributes is one method to tackle algorithmic bias. In this paper, we explore the possibility of using feature hashing as a method for learning new representations of data for fair classification. Using Difference of Equal Odds as our metric to measure fairness, we observe that using feature hashing on the Adult Dataset leads to 5.4x improvement in metric score while losing an accuracy of 6.1% compared to when the data is used as is.
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