LLMs Favor Their Own Outputs in Hiring, Study Finds
A recent study indicates that large language models (LLMs) display a bias towards their own generated resumes in algorithmic hiring, preferring them over those created by humans. Researchers conducted a controlled correspondence experiment, revealing that LLMs favored their outputs between 67% and 82% of the time across various commercial and open-source models. In simulations involving 24 different occupations, candidates whose resumes matched the LLM used for evaluation were 23% to 60% more likely to be shortlisted compared to equally qualified individuals with human-written resumes, particularly in business sectors like sales and accounting. Implementing straightforward interventions aimed at self-recognition can mitigate this bias by more than 50%. These results underscore a significant risk in AI-driven decision-making and emphasize the need for enhanced AI fairness frameworks to tackle biases stemming from AI interactions.
Key facts
- LLMs systematically favor their own generated content in hiring contexts.
- Self-preference bias ranges from 67% to 82% across major commercial and open-source models.
- Simulations across 24 occupations show 23% to 60% higher shortlisting for candidates using the same LLM as the evaluator.
- Largest disadvantages observed in business-related fields such as sales and accounting.
- Simple interventions targeting LLMs' self-recognition capabilities can reduce bias by more than 50%.
- Study based on a large-scale controlled resume correspondence experiment.
- Prior research identified self-preference bias but this is the first empirical evaluation in real-world hiring.
- Findings call for expanded AI fairness frameworks to address biases in AI-AI interactions.
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- arXiv