# Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey

arXiv: https://arxiv.org/abs/2309.13933 · HTML: https://mlsanity.com/paper/2309.13933/

## 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 (most cited first, 11 of 11)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [A Survey on the Fairness of Recommender Systems](https://mlsanity.com/paper/2206.03761/) (2022)
- [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021)
- [Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination](https://mlsanity.com/paper/1408.6491/) (2014)
- [Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI](https://mlsanity.com/paper/2005.05906/) (2020)
- [Algorithmic Fairness Datasets: the Story so Far](https://mlsanity.com/paper/2202.01711/) (2022)
- [An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring](https://mlsanity.com/paper/2201.09151/) (2022)
- [Gender Stereotype Reinforcement: Measuring the Gender Bias Conveyed by Ranking Algorithms](https://mlsanity.com/paper/2009.01334/) (2020)
- [Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach](https://mlsanity.com/paper/2109.08549/) (2021)
- [Using sensitive data to prevent discrimination by artificial intelligence: Does the GDPR need a new exception?](https://mlsanity.com/paper/2206.03262/) (2022)

## Built on by (most cited first, 30 of 30)

- [Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education](https://mlsanity.com/paper/2503.19182/) (2025)
- [Lazy Data Practices Harm Fairness Research](https://mlsanity.com/paper/2404.17293/) (2024)
- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Human Autonomy](https://mlsanity.com/paper/2509.04404/) (2025)
- [Invisible Filters: Cultural Bias in Hiring Evaluations Using Large Language Models](https://mlsanity.com/paper/2508.16673/) (2025)
- [Fragile Preferences: A Deep Dive Into Order Effects in Large Language Models](https://mlsanity.com/paper/2506.14092/) (2025)
- [From Benchmarks to Business Impact: Deploying IBM Generalist Agent in Enterprise Production](https://mlsanity.com/paper/2510.23856/) (2025)
- [Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statements](https://mlsanity.com/paper/2411.07845/) (2024)
- [Societal Impacts Research Requires Benchmarks for Creative Composition Tasks](https://mlsanity.com/paper/2504.06549/) (2025)
- [AI Security Beyond Core Domains: Resume Screening as a Case Study of Adversarial Vulnerabilities in Specialized LLM Applications](https://mlsanity.com/paper/2512.20164/) (2025)
- [Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Exploration](https://mlsanity.com/paper/2505.00802/) (2025)
- [Does Reasoning Make Search More Fair? Comparing Fairness in Reasoning and Non-Reasoning Rerankers](https://mlsanity.com/paper/2603.10332/) (2026)
- [WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain](https://mlsanity.com/paper/2604.13055/) (2026)
- [Humble AI in the real-world: the case of algorithmic hiring](https://mlsanity.com/paper/2505.20918/) (2025)
- [Beyond Explainability: The Case for AI Validation](https://mlsanity.com/paper/2505.21570/) (2025)
- [Mitigation of Gender and Ethnicity Bias in AI-Generated Stories through Model Explanations](https://mlsanity.com/paper/2509.04515/) (2025)
- [Explained, yet misunderstood: How AI Literacy shapes HR Managers' interpretation of User Interfaces in Recruiting Recommender Systems](https://mlsanity.com/paper/2509.06475/) (2025)
- [Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiring](https://mlsanity.com/paper/2604.19984/) (2026)
- [A Tutorial On Intersectionality in Fair Rankings](https://mlsanity.com/paper/2502.05333/) (2025)
- [OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System](https://mlsanity.com/paper/2504.07108/) (2025)
- [A Gray Literature Study on Fairness Requirements in AI-enabled Software Engineering](https://mlsanity.com/paper/2512.07990/) (2025)
- [AutoScreen-FW: An LLM-based Framework for Resume Screening](https://mlsanity.com/paper/2603.18390/) (2026)
- [Explainable Speech Emotion Recognition: Weighted Attribute Fairness to Model Demographic Contributions to Social Bias](https://mlsanity.com/paper/2604.19763/) (2026)
- [How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applications](https://mlsanity.com/paper/2604.22679/) (2026)
- [Improving Selective Classification with Pairwise Queries for Binary Classification](https://mlsanity.com/paper/2605.30615/) (2026)
- [Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-off](https://mlsanity.com/paper/2606.28097/) (2026)
- [AI-Augmented Human Resource Management? Insights from German companies](https://mlsanity.com/paper/2607.13839/) (2026)
- [Fairness Auditing: Lower Bounds on Company Manipulation](https://mlsanity.com/paper/2608.00568/) (2026)
- [Counterfactual Bias Testing for Application Tracking System](https://mlsanity.com/paper/2608.26899/) (2026)
- [Fairness Beyond Anonymization? Demographic Leakage in German LLM-Generated Resumes](https://mlsanity.com/paper/2609.22188/) (2026)

## 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.