LLMs Favor Their Own Resumes

In controlled tests, resume‑screening LLMs preferred resumes generated by themselves over equally qualified human‑written or other‑model resumes. Self‑preference bias ran 68%–88% across major models, boosting shortlists 23%–60% for applicants who used the same LLM as the evaluator. Simple prompts/filters halved the bias. — This reveals a hidden source of AI hiring unfairness and an arms race incentive to match the employer’s model, pushing regulators and firms to standardize or neutralize screening systems.

Sources

AI: Queer Lives Matter, Straight Lives Don't

Arctotherium 2025.12.03 72% relevant

Both pieces document evaluative biases in LLMs: the existing idea shows models prefer resumes produced by themselves, while the article reports models assigning higher moral value to people with particular political identities (environmentalists/socialists/communists). Together they point to a broader problem of LLMs producing systematic, non‑transparent preference skews that can distort hiring, moral judgments, and other downstream decisions (the article names Claude as an actor exhibiting a strong preference).

Do LLMs favor outputs created by themselves?

Tyler Cowen 2025.10.03 100% relevant

Paper by Jiannan Xu, Gujie Li, and Jane Yi Jiant reporting self‑preference bias and mitigation in resume screening across 24 occupations.