{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T21:04:03Z","timestamp":1778619843894,"version":"3.51.4"},"reference-count":86,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T00:00:00Z","timestamp":1696550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"abstract":"<jats:p>Recommender systems (RSs) have become an integral part of the hiring process, be it via job advertisement ranking systems (job recommenders) for the potential employee or candidate ranking systems (candidate recommenders) for the employer. As seen in other domains, RSs are prone to harmful biases, unfair algorithmic behavior, and even discrimination in a legal sense. Some cases, such as salary equity in regards to gender (gender pay gap), stereotypical job perceptions along gendered lines, or biases toward other subgroups sharing specific characteristics in candidate recommenders, can have profound ethical and legal implications. In this survey, we discuss the current state of fairness research considering the fairness definitions (e.g., demographic parity and equal opportunity) used in recruitment-related RSs (RRSs). We investigate from a technical perspective the approaches to improve fairness, like synthetic data generation, adversarial training, protected subgroup distributional constraints, and <jats:italic>post-hoc<\/jats:italic> re-ranking. Thereafter, from a legal perspective, we contrast the fairness definitions and the effects of the aforementioned approaches with existing EU and US law requirements for employment and occupation, and second, we ascertain whether and to what extent EU and US law permits such approaches to improve fairness. We finally discuss the advances that RSs have made in terms of fairness in the recruitment domain, compare them with those made in other domains, and outline existing open challenges.<\/jats:p>","DOI":"10.3389\/fdata.2023.1245198","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T09:51:40Z","timestamp":1696585900000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives"],"prefix":"10.3389","volume":"6","author":[{"given":"Deepak","family":"Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tessa","family":"Grosz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Navid","family":"Rekabsaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elisabeth","family":"Greif","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markus","family":"Schedl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,10,6]]},"reference":[{"key":"B1","first-page":"425","article-title":"\u201cRecsys challenge 2016: Job recommendations,\u201d","volume-title":"Proceedings of the 10th ACM Conference on Recommender Systems, RecSys '16","author":"Abel","year":"2016"},{"key":"B2","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1145\/3109859.3109954","article-title":"\u201cRecsys challenge 2017: Offline and online evaluation,\u201d","volume-title":"Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys '17","author":"Abel","year":"2017"},{"key":"B3","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1177\/20319525211062558","article-title":"Regulating algorithms at work: Lessons for a \u201cEuropean approach to artificial intelligence.\u201d","volume":"13","author":"Adams-Prassl","year":"2022"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3359301","article-title":"Discrimination through optimization: How Facebook's ad delivery can lead to biased outcomes","volume":"3","author":"Ali","year":"2019","journal-title":"Proc. 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