Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey

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.

Alessandro Fabris, Nina Baranowska, Matthew J. Dennis, David Graus, Philipp Hacker, Jorge Saldivar, Frederik Zuiderveen Borgesius, and Asia J. Biega. Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey. ACM Transactions on Intelligent Systems and Technology. 2025. https://doi.org/10.1145/3696457

References in corpus (24)

• Artificial Intelligence: the global landscape of ethics guidelines
• Training language models to follow instructions with human feedback
• Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
• Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions
• A Survey on the Fairness of Recommender Systems
• Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
• Fairness Constraints: Mechanisms for Fair Classification
• 50 Years of Test (Un)fairness: Lessons for Machine Learning
• Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
• Discrimination through optimization: How Facebook's ad delivery can lead to skewed outcomes
• Measurement and Fairness
• Auditing for Discrimination in Algorithms Delivering Job Ads
• Fairness in Ranking: A Survey
• Using sensitive data to prevent discrimination by artificial intelligence: Does the GDPR need a new exception?
• LiFT: A Scalable Framework for Measuring Fairness in ML Applications
• On the Moral Justification of Statistical Parity
• Gaps in Information Access in Social Networks
• The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendations
• Algorithmic Decision Making with Conditional Fairness
• Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints
• Lazy Data Practices Harm Fairness Research
• Disentangling and Operationalizing AI Fairness at LinkedIn
• Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation
• "You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning

Cited by in corpus (3)

• Lazy Data Practices Harm Fairness Research
• Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring
• Resume Screening, Fast and Slow: (Biased) AI Recommendations' Influence on Human Decision Making