Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey

TL;DR

This survey reviews the current state of algorithmic fairness in hiring, discussing biases, mitigation strategies, legal issues, and future directions to promote equitable and trustworthy employment technologies.

Contribution

It provides a comprehensive, multidisciplinary overview of algorithmic hiring fairness, integrating technical, ethical, and legal perspectives to guide future research and practice.

Findings

Current algorithms exhibit biases that can perpetuate inequalities.

Mitigation strategies include data balancing and fairness-aware modeling.

Legal frameworks are evolving to regulate algorithmic hiring practices.

Abstract

Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination. Whether, and more importantly what types of, algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness. This multidisciplinary survey caters to practitioners and researchers with a balanced and integrated coverage of systems, biases, measures, mitigation strategies, datasets, and legal aspects of algorithmic hiring and fairness. Our work supports a contextualized understanding and governance of this technology by highlighting current opportunities and limitations, providing recommendations for future work to ensure shared benefits for all stakeholders.