{"title":"数据科学中的偏斜分布","authors":"N. Dasgupta","doi":"10.1080/09332480.2022.2039034","DOIUrl":null,"url":null,"abstract":"This column is about raising questions, rather than providing answers. These days “data based decision making” is the rage among administrators in both industry and academia. The desire for this dependence on algorithms stems from the general idea that “humans are biased but machines are not”. More and more social decisions, like qualifying for welfare are, are made using algorithms. With this, the data scientists, who are behind the algorithms, are given a lot of power (and responsibility.) In this column, I discuss demographic characteristics of data scientists with conjectures on why this group is non-diverse. Should we allow a small group of non-representative people to make decisions that affect affect larger society?","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"22 1","pages":"51 - 55"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Skewed Distributions in Data Science\",\"authors\":\"N. Dasgupta\",\"doi\":\"10.1080/09332480.2022.2039034\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This column is about raising questions, rather than providing answers. These days “data based decision making” is the rage among administrators in both industry and academia. The desire for this dependence on algorithms stems from the general idea that “humans are biased but machines are not”. More and more social decisions, like qualifying for welfare are, are made using algorithms. With this, the data scientists, who are behind the algorithms, are given a lot of power (and responsibility.) In this column, I discuss demographic characteristics of data scientists with conjectures on why this group is non-diverse. Should we allow a small group of non-representative people to make decisions that affect affect larger society?\",\"PeriodicalId\":88226,\"journal\":{\"name\":\"Chance (New York, N.Y.)\",\"volume\":\"22 1\",\"pages\":\"51 - 55\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chance (New York, N.Y.)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/09332480.2022.2039034\",\"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/10.1080/09332480.2022.2039034","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This column is about raising questions, rather than providing answers. These days “data based decision making” is the rage among administrators in both industry and academia. The desire for this dependence on algorithms stems from the general idea that “humans are biased but machines are not”. More and more social decisions, like qualifying for welfare are, are made using algorithms. With this, the data scientists, who are behind the algorithms, are given a lot of power (and responsibility.) In this column, I discuss demographic characteristics of data scientists with conjectures on why this group is non-diverse. Should we allow a small group of non-representative people to make decisions that affect affect larger society?