{"title":"Applying a Trustworthy AI Framework to Mitigate Bias and Increase Workforce Gender Diversity","authors":"X. Liu, Diane R. Murphy","doi":"10.1109/ISTAS55053.2022.10227119","DOIUrl":null,"url":null,"abstract":"Organizations increasingly use artificial intelligence (AI) technologies in their screening and recruiting process. However, AI-enabled recruiting and talent management tools have also introduced risks of unfair bias that may compromise workforce diversity. This conceptual paper frames an ethical discussion regarding gender equity in AI-enabled workforce decision applications. The authors examined several real-world cases in which AI was used in talent acquisition. The ensuing question is whether the use of AI in the hiring process can be turned into an advantage to improve gender equity. To address the question, a multi-faceted trustworthy AI framework was reviewed and applied to the workforce decision context. A list of implementation guidelines is proposed to mitigate bias and increase diversity in the IT workforce. The authors aim to stimulate further discussions and investigation on this complex topic and to call for action to develop educational programs or awareness campaigns on trustworthy AI, so improving gender equity in technology hiring.","PeriodicalId":180420,"journal":{"name":"2022 IEEE International Symposium on Technology and Society (ISTAS)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Symposium on Technology and Society (ISTAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISTAS55053.2022.10227119","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Organizations increasingly use artificial intelligence (AI) technologies in their screening and recruiting process. However, AI-enabled recruiting and talent management tools have also introduced risks of unfair bias that may compromise workforce diversity. This conceptual paper frames an ethical discussion regarding gender equity in AI-enabled workforce decision applications. The authors examined several real-world cases in which AI was used in talent acquisition. The ensuing question is whether the use of AI in the hiring process can be turned into an advantage to improve gender equity. To address the question, a multi-faceted trustworthy AI framework was reviewed and applied to the workforce decision context. A list of implementation guidelines is proposed to mitigate bias and increase diversity in the IT workforce. The authors aim to stimulate further discussions and investigation on this complex topic and to call for action to develop educational programs or awareness campaigns on trustworthy AI, so improving gender equity in technology hiring.