{"title":"劳动力pDEI:生产力与DEI相结合","authors":"Lanqing Du, Jinwook Lee","doi":"arxiv-2311.11231","DOIUrl":null,"url":null,"abstract":"Ranking pertaining to the human-centered tasks -- underscoring their\nparamount significance in these domains such as evaluation and hiring process\n-- exhibits widespread prevalence across various industries. Consequently,\ndecision-makers are taking proactive measurements to promote diversity,\nunderscore equity, and advance inclusion. Their unwavering commitment to these\nideals emanates from the following convictions: (i) Diversity encompasses a\nbroad spectrum of differences; (ii) Equity involves the assurance of equitable\nopportunities; and (iii) Inclusion revolves around the cultivation of a sense\nof value and impartiality, concurrently empowering individuals. Data-driven AI\ntools have been used for screening and ranking processes. However, there is a\ngrowing concern that the presence of pre-existing biases in databases may be\nexacerbated, particularly in the context of imbalanced datasets or the\nblack-box-schema. In this research, we propose a model-driven recruitment\ndecision support tool that addresses fairness together with equity in the\nscreening phase. We introduce the term ``pDEI\" to represent the output-input\noriented production efficiency adjusted by socioeconomic disparity. Taking into\naccount various aspects of interpreting socioeconomic disparity, our goals are\n(i) maximizing the relative efficiency of underrepresented groups and (ii)\nunderstanding how socioeconomic disparity affects the cultivation of a\nDEI-positive workplace.","PeriodicalId":501487,"journal":{"name":"arXiv - QuantFin - Economics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Workforce pDEI: Productivity Coupled with DEI\",\"authors\":\"Lanqing Du, Jinwook Lee\",\"doi\":\"arxiv-2311.11231\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ranking pertaining to the human-centered tasks -- underscoring their\\nparamount significance in these domains such as evaluation and hiring process\\n-- exhibits widespread prevalence across various industries. Consequently,\\ndecision-makers are taking proactive measurements to promote diversity,\\nunderscore equity, and advance inclusion. Their unwavering commitment to these\\nideals emanates from the following convictions: (i) Diversity encompasses a\\nbroad spectrum of differences; (ii) Equity involves the assurance of equitable\\nopportunities; and (iii) Inclusion revolves around the cultivation of a sense\\nof value and impartiality, concurrently empowering individuals. Data-driven AI\\ntools have been used for screening and ranking processes. However, there is a\\ngrowing concern that the presence of pre-existing biases in databases may be\\nexacerbated, particularly in the context of imbalanced datasets or the\\nblack-box-schema. In this research, we propose a model-driven recruitment\\ndecision support tool that addresses fairness together with equity in the\\nscreening phase. We introduce the term ``pDEI\\\" to represent the output-input\\noriented production efficiency adjusted by socioeconomic disparity. Taking into\\naccount various aspects of interpreting socioeconomic disparity, our goals are\\n(i) maximizing the relative efficiency of underrepresented groups and (ii)\\nunderstanding how socioeconomic disparity affects the cultivation of a\\nDEI-positive workplace.\",\"PeriodicalId\":501487,\"journal\":{\"name\":\"arXiv - QuantFin - Economics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuantFin - Economics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2311.11231\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2311.11231","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ranking pertaining to the human-centered tasks -- underscoring their
paramount significance in these domains such as evaluation and hiring process
-- exhibits widespread prevalence across various industries. Consequently,
decision-makers are taking proactive measurements to promote diversity,
underscore equity, and advance inclusion. Their unwavering commitment to these
ideals emanates from the following convictions: (i) Diversity encompasses a
broad spectrum of differences; (ii) Equity involves the assurance of equitable
opportunities; and (iii) Inclusion revolves around the cultivation of a sense
of value and impartiality, concurrently empowering individuals. Data-driven AI
tools have been used for screening and ranking processes. However, there is a
growing concern that the presence of pre-existing biases in databases may be
exacerbated, particularly in the context of imbalanced datasets or the
black-box-schema. In this research, we propose a model-driven recruitment
decision support tool that addresses fairness together with equity in the
screening phase. We introduce the term ``pDEI" to represent the output-input
oriented production efficiency adjusted by socioeconomic disparity. Taking into
account various aspects of interpreting socioeconomic disparity, our goals are
(i) maximizing the relative efficiency of underrepresented groups and (ii)
understanding how socioeconomic disparity affects the cultivation of a
DEI-positive workplace.