Sebastien Delecraz , Loukman Eltarr , Martin Becuwe , Henri Bouxin , Nicolas Boutin , Olivier Oullier
{"title":"人力资源技术中的负责任人工智能:一种创新的包容性和公平性匹配算法,用于招聘目的","authors":"Sebastien Delecraz , Loukman Eltarr , Martin Becuwe , Henri Bouxin , Nicolas Boutin , Olivier Oullier","doi":"10.1016/j.jrt.2022.100041","DOIUrl":null,"url":null,"abstract":"<div><p>In this article, we address the broad issue of a responsible use of Artificial Intelligence in Human Resources Management through the lens of a fair-by-design approach to algorithm development illustrated by the introduction of a new machine learning-based approach to job matching. The goal of our algorithmic solution is to improve and automate the recruitment of temporary workers to find the best match with existing job offers. We discuss how fairness should be a key focus of human resources management and highlight the main challenges and flaws in the research that arise when developing algorithmic solutions to match candidates with job offers. After an in-depth analysis of the distribution and biases of our proprietary data set, we describe the methodology used to evaluate the effectiveness and fairness of our machine learning model as well as solutions to correct some biases. The model we introduce constitutes the first step in our effort to control for unfairness in the outcomes of machine learning algorithms in job recruitment, and more broadly a responsible use of artificial intelligence in Human Resources Management thanks to “safeguard algorithms” tasked to control for biases and prevent discriminatory outcomes.</p></div>","PeriodicalId":73937,"journal":{"name":"Journal of responsible technology","volume":"11 ","pages":"Article 100041"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266665962200018X/pdfft?md5=1067842485c764fe87523992da73aaec&pid=1-s2.0-S266665962200018X-main.pdf","citationCount":"6","resultStr":"{\"title\":\"Responsible Artificial Intelligence in Human Resources Technology: An innovative inclusive and fair by design matching algorithm for job recruitment purposes\",\"authors\":\"Sebastien Delecraz , Loukman Eltarr , Martin Becuwe , Henri Bouxin , Nicolas Boutin , Olivier Oullier\",\"doi\":\"10.1016/j.jrt.2022.100041\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this article, we address the broad issue of a responsible use of Artificial Intelligence in Human Resources Management through the lens of a fair-by-design approach to algorithm development illustrated by the introduction of a new machine learning-based approach to job matching. The goal of our algorithmic solution is to improve and automate the recruitment of temporary workers to find the best match with existing job offers. We discuss how fairness should be a key focus of human resources management and highlight the main challenges and flaws in the research that arise when developing algorithmic solutions to match candidates with job offers. After an in-depth analysis of the distribution and biases of our proprietary data set, we describe the methodology used to evaluate the effectiveness and fairness of our machine learning model as well as solutions to correct some biases. The model we introduce constitutes the first step in our effort to control for unfairness in the outcomes of machine learning algorithms in job recruitment, and more broadly a responsible use of artificial intelligence in Human Resources Management thanks to “safeguard algorithms” tasked to control for biases and prevent discriminatory outcomes.</p></div>\",\"PeriodicalId\":73937,\"journal\":{\"name\":\"Journal of responsible technology\",\"volume\":\"11 \",\"pages\":\"Article 100041\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S266665962200018X/pdfft?md5=1067842485c764fe87523992da73aaec&pid=1-s2.0-S266665962200018X-main.pdf\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of responsible technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S266665962200018X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of responsible technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266665962200018X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Responsible Artificial Intelligence in Human Resources Technology: An innovative inclusive and fair by design matching algorithm for job recruitment purposes
In this article, we address the broad issue of a responsible use of Artificial Intelligence in Human Resources Management through the lens of a fair-by-design approach to algorithm development illustrated by the introduction of a new machine learning-based approach to job matching. The goal of our algorithmic solution is to improve and automate the recruitment of temporary workers to find the best match with existing job offers. We discuss how fairness should be a key focus of human resources management and highlight the main challenges and flaws in the research that arise when developing algorithmic solutions to match candidates with job offers. After an in-depth analysis of the distribution and biases of our proprietary data set, we describe the methodology used to evaluate the effectiveness and fairness of our machine learning model as well as solutions to correct some biases. The model we introduce constitutes the first step in our effort to control for unfairness in the outcomes of machine learning algorithms in job recruitment, and more broadly a responsible use of artificial intelligence in Human Resources Management thanks to “safeguard algorithms” tasked to control for biases and prevent discriminatory outcomes.