Cargando…

Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives

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, u...

Descripción completa

Detalles Bibliográficos
Autores principales: Kumar, Deepak, Grosz, Tessa, Rekabsaz, Navid, Greif, Elisabeth, Schedl, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10587596/
https://www.ncbi.nlm.nih.gov/pubmed/37869249
http://dx.doi.org/10.3389/fdata.2023.1245198
_version_ 1785123400522399744
author Kumar, Deepak
Grosz, Tessa
Rekabsaz, Navid
Greif, Elisabeth
Schedl, Markus
author_facet Kumar, Deepak
Grosz, Tessa
Rekabsaz, Navid
Greif, Elisabeth
Schedl, Markus
author_sort Kumar, Deepak
collection PubMed
description 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 post-hoc 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.
format Online
Article
Text
id pubmed-10587596
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-105875962023-10-21 Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives Kumar, Deepak Grosz, Tessa Rekabsaz, Navid Greif, Elisabeth Schedl, Markus Front Big Data Big Data 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 post-hoc 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. Frontiers Media S.A. 2023-10-06 /pmc/articles/PMC10587596/ /pubmed/37869249 http://dx.doi.org/10.3389/fdata.2023.1245198 Text en Copyright © 2023 Kumar, Grosz, Rekabsaz, Greif and Schedl. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Kumar, Deepak
Grosz, Tessa
Rekabsaz, Navid
Greif, Elisabeth
Schedl, Markus
Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives
title Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives
title_full Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives
title_fullStr Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives
title_full_unstemmed Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives
title_short Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives
title_sort fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10587596/
https://www.ncbi.nlm.nih.gov/pubmed/37869249
http://dx.doi.org/10.3389/fdata.2023.1245198
work_keys_str_mv AT kumardeepak fairnessofrecommendersystemsintherecruitmentdomainananalysisfromtechnicalandlegalperspectives
AT grosztessa fairnessofrecommendersystemsintherecruitmentdomainananalysisfromtechnicalandlegalperspectives
AT rekabsaznavid fairnessofrecommendersystemsintherecruitmentdomainananalysisfromtechnicalandlegalperspectives
AT greifelisabeth fairnessofrecommendersystemsintherecruitmentdomainananalysisfromtechnicalandlegalperspectives
AT schedlmarkus fairnessofrecommendersystemsintherecruitmentdomainananalysisfromtechnicalandlegalperspectives