{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:59:38Z","timestamp":1784221178370,"version":"3.55.0"},"reference-count":114,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T00:00:00Z","timestamp":1590624000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,5,28]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this article, we examine the state-of-the-art and current applications of artificial intelligence (AI), specifically for human resources (HR). We study whether, due to the experimental state of the algorithms used and the nature of training and test samples, a further control and auditing in the research community is necessary to guarantee fair and accurate results. In particular, we identify the positive and negative consequences of the usage of video-interview analysis via AI in recruiting processes as well as the main machine learning techniques used and their degrees of efficiency. We focus on some controversial characteristics that could lead to ethical and legal consequences for candidates, companies and states regarding discrimination in the job market (e.g. gender and race). There is a lack of regulation and a need for external and neutral auditing for the type of analyses done in interviews. We present a multi-agent architecture that aims at total legal compliance and more effective HR processes management.<\/jats:p>","DOI":"10.1515\/pjbr-2020-0030","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T09:03:15Z","timestamp":1591088595000},"page":"199-216","source":"Crossref","is-referenced-by-count":51,"title":["AI and recruiting software: Ethical and legal implications"],"prefix":"10.1515","volume":"11","author":[{"given":"Carmen","family":"Fern\u00e1ndez-Mart\u00ednez","sequence":"first","affiliation":[{"name":"CETINIA, University Rey Juan Carlos, Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"CETINIA, University Rey Juan Carlos, Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","reference":[{"key":"ref21","article-title":"\u201cArtificial intelligence techniques in human resource management \u2013 a conceptual exploration,\u201d","volume-title":"Intelligent Techniques in Engineering Management","volume":"vol. 87","year":"2015"},{"key":"ref1061","doi-asserted-by":"crossref","first-page":"pp. 41","DOI":"10.2328\/jnds.34.41","article-title":"\u201cAgent-based simulation of the 2011 great east Japan earthquake\/tsunami evacuation: an integrated model of tsunami inundation and evacuation,\u201d","volume":"vol. 34","year":"2012","journal-title":"J. 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Psychol."},{"key":"ref681","article-title":"\u201cIranian face database with age, pose and expression,\u201d","year":"Dec. 2007","journal-title":"2007 International Conference on Machine Vision"},{"key":"ref341","article-title":"\u201cRecognition of emotion intensities using machine learning algorithms: a comparative study,\u201d","volume":"vol. 19","year":"2019","journal-title":"Sensors"},{"key":"ref541","article-title":"\u201cPreventing fairness gerrymandering: auditing and learning for subgroup fairness,\u201d","year":"2017","journal-title":"arXiv preprint arXiv:1711.05144"},{"key":"ref71","doi-asserted-by":"crossref","first-page":"pp. 2483","DOI":"10.1109\/TPAMI.2014.2321570","article-title":"\u201cLearning race from face: a survey,\u201d","volume":"vol. 36","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref441","article-title":"\u201cResearchers use facial recognition tools to predict sexual orientation. 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