# Fairness in AI-Driven Recruitment: Challenges, Metrics, Methods, and Future Directions

arXiv: https://arxiv.org/abs/2405.19699 · HTML: https://mlsanity.com/paper/2405.19699/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2024.

## Built on by (most cited first, 5 of 5)

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- [Identifying and Mitigating Gender Cues in Academic Recommendation Letters: An Interpretability Case Study](https://mlsanity.com/paper/2604.12337/) (2026)
- [Signal or Noise? Evaluating Large Language Models in Resume Screening Across Contextual Variations and Human Expert Benchmarks](https://mlsanity.com/paper/2507.08019/) (2025)
- [SETU: An Agentic Ecosystem for Multilingual, Persona-Aware Communication Coaching](https://mlsanity.com/paper/2608.27524/) (2026)
- [Fairness Beyond Anonymization? Demographic Leakage in German LLM-Generated Resumes](https://mlsanity.com/paper/2609.22188/) (2026)

## Abstract (arXiv)

The recruitment process significantly impacts an organization's performance, productivity, and culture. Traditionally, human resource experts and industrial-organizational psychologists have developed systematic hiring methods, including job advertising, candidate skill assessments, and structured interviews to ensure candidate-organization fit. Recently, recruitment practices have shifted dramatically toward artificial intelligence (AI)-based methods, driven by the need to efficiently manage large applicant pools. However, reliance on AI raises concerns about the amplification and propagation of human biases embedded within hiring algorithms, as empirically demonstrated by biases in candidate ranking systems and automated interview assessments. Consequently, algorithmic fairness has emerged as a critical consideration in AI-driven recruitment, aimed at rigorously addressing and mitigating these biases. This paper systematically reviews biases identified in AI-driven recruitment systems, categorizes fairness metrics and bias mitigation techniques, and highlights auditing approaches used in practice. We emphasize critical gaps and current limitations, proposing future directions to guide researchers and practitioners toward more equitable AI recruitment practices, promoting fair candidate treatment and enhancing organizational outcomes.