{"title":"风险","authors":"M. Gray","doi":"10.1080/09332480.2022.2145134","DOIUrl":null,"url":null,"abstract":"Should different people be treated equally or should, as insurers tell us, different people be treated differently? Is discrimination bad, or is it good? Does today’s reliance on machine-generated algorithms to turn risk into measurable uncertainty differ in essence from trusting the actuarial tables generated by de Moivre from a London coffee house? Do legal regulations assure fair balance of the costs and benefits? What about inclusive social insurance?","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"27 1","pages":"36 - 39"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Risk\",\"authors\":\"M. Gray\",\"doi\":\"10.1080/09332480.2022.2145134\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Should different people be treated equally or should, as insurers tell us, different people be treated differently? Is discrimination bad, or is it good? Does today’s reliance on machine-generated algorithms to turn risk into measurable uncertainty differ in essence from trusting the actuarial tables generated by de Moivre from a London coffee house? Do legal regulations assure fair balance of the costs and benefits? What about inclusive social insurance?\",\"PeriodicalId\":88226,\"journal\":{\"name\":\"Chance (New York, N.Y.)\",\"volume\":\"27 1\",\"pages\":\"36 - 39\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chance (New York, N.Y.)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/09332480.2022.2145134\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chance (New York, N.Y.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/09332480.2022.2145134","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Should different people be treated equally or should, as insurers tell us, different people be treated differently? Is discrimination bad, or is it good? Does today’s reliance on machine-generated algorithms to turn risk into measurable uncertainty differ in essence from trusting the actuarial tables generated by de Moivre from a London coffee house? Do legal regulations assure fair balance of the costs and benefits? What about inclusive social insurance?