LLMs consistently pick resumes they generate over ones by humans or other models
5 hours ago
- #Algorithmic hiring
- #AI bias
- #Self-preferencing
- AI tools are increasingly used in decision-making processes such as hiring and content moderation.
- Large language models (LLMs) exhibit self-preference bias, favoring their own generated content over human-written or alternative model outputs.
- In hiring contexts, LLMs prefer resumes they generate, with bias ranging from 67% to 82%, disadvantaging human-written resumes.
- Candidates using the same LLM as evaluators are 23% to 60% more likely to be shortlisted than those submitting human-written resumes.
- Bias is particularly significant in business-related fields like sales and accounting.
- Simple interventions can reduce self-preference bias by over 50% by targeting LLMs' self-recognition capabilities.
- Findings highlight a need for expanded AI fairness frameworks to address biases in AI-AI interactions beyond demographic disparities.