mlsanityData to 14 Oct 2026

Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey

arXiv 2309.13933 ↗First posted 25 Sep 2023cs.CY cs.AIcited by 30

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Abstract (arXiv)

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.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Builds on 11 of its 105 references that are in the map. First posted on arXiv 25 Sep 2023.

Builds on

11 of 105 references.

References · 11

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
  3. 2022A Survey on the Fairness of Recommender Systemscited by 102
  4. 2021Trustworthy AI: A Computational Perspectivecited by 93
  5. 2014Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discriminationcited by 83
  6. 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
  7. 2022Algorithmic Fairness Datasets: the Story so Farcited by 51
  8. 2022An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiringcited by 10
  9. 2020Gender Stereotype Reinforcement: Measuring the Gender Bias Conveyed by Ranking Algorithmscited by 9
  10. 2021Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approachcited by 9
  11. 2022Using sensitive data to prevent discrimination by artificial intelligence: Does the GDPR need a new exception?cited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 15; 2026: 12.

30 papers.

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  1. 2025Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Educationcited by 14
  2. 2024Lazy Data Practices Harm Fairness Researchcited by 10
  3. 2024Natural Language Processing for Human Resources: A Surveycited by 10
  4. 2025No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Human Autonomycited by 8
  5. 2025Invisible Filters: Cultural Bias in Hiring Evaluations Using Large Language Modelscited by 6
  6. 2025Fragile Preferences: A Deep Dive Into Order Effects in Large Language Modelscited by 5
  7. 2025From Benchmarks to Business Impact: Deploying IBM Generalist Agent in Enterprise Productioncited by 5
  8. 2024Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statementscited by 3
  9. 2025Societal Impacts Research Requires Benchmarks for Creative Composition Taskscited by 3
  10. 2025AI Security Beyond Core Domains: Resume Screening as a Case Study of Adversarial Vulnerabilities in Specialized LLM Applicationscited by 3
  11. 2025Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Explorationcited by 2
  12. 2026Does Reasoning Make Search More Fair? Comparing Fairness in Reasoning and Non-Reasoning Rerankerscited by 2
18 more
  1. 2026WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domaincited by 2
  2. 2025Humble AI in the real-world: the case of algorithmic hiringcited by 1
  3. 2025Beyond Explainability: The Case for AI Validationcited by 1
  4. 2025Mitigation of Gender and Ethnicity Bias in AI-Generated Stories through Model Explanationscited by 1
  5. 2025Explained, yet misunderstood: How AI Literacy shapes HR Managers' interpretation of User Interfaces in Recruiting Recommender Systemscited by 1
  6. 2026Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiringcited by 1
  7. 2025A Tutorial On Intersectionality in Fair Rankingscited by 0
  8. 2025OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender Systemcited by 0
  9. 2025A Gray Literature Study on Fairness Requirements in AI-enabled Software Engineeringcited by 0
  10. 2026AutoScreen-FW: An LLM-based Framework for Resume Screeningcited by 0
  11. 2026Explainable Speech Emotion Recognition: Weighted Attribute Fairness to Model Demographic Contributions to Social Biascited by 0
  12. 2026How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applicationscited by 0
  13. 2026Improving Selective Classification with Pairwise Queries for Binary Classificationcited by 0
  14. 2026Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-offcited by 0
  15. 2026AI-Augmented Human Resource Management? Insights from German companiescited by 0
  16. 2026Fairness Auditing: Lower Bounds on Company Manipulationcited by 0
  17. 2026Counterfactual Bias Testing for Application Tracking Systemcited by 0
  18. 2026Fairness Beyond Anonymization? Demographic Leakage in German LLM-Generated Resumescited by 0