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

Frontiers in Big Data

Big Data

Tessa Grosz¹  Elisabeth Greif¹  Deepak Kumar²  Navid Rekabsaz³  Markus Schedl³ 

[1] Institute for Legal Gender Studies, Johannes Kepler University Linz, Linz, Austria;Multimedia Mining and Search Group, Institute of Computational Perception, Johannes Kepler University Linz, Linz, Austria;Multimedia Mining and Search Group, Institute of Computational Perception, Johannes Kepler University Linz, Linz, Austria;Human-centered AI Group, AI Lab, Linz Institute of Technology, Linz, Austria;

关键词: recommender system;    recruitment;    job recommendation;    candidate recommendation;    fairness;    discrimination;    law;   

DOI  :  10.3389/fdata.2023.1245198

 received in 2023-06-23, accepted in 2023-09-18,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Kumar, Grosz, Rekabsaz, Greif and Schedl.