AI prefers resumes written by itself: Self-preferencing in Algorithmic Hiring
A recent study explores the self-preferencing bias of large language models (LLMs) in algorithmic hiring. The research indicates that LLMs favor resumes generated by themselves over those written by humans, with a significant bias observed. This raises concerns about fairness in AI-assisted decision-making processes, particularly in hiring contexts.
- ▪LLMs consistently prefer resumes generated by themselves over human-written ones.
- ▪The self-preference bias ranges from 67% to 82% across various models.
- ▪Candidates using the same LLM as the evaluator are 23% to 60% more likely to be shortlisted.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2509.00462 |
| Publication time | Tue, 28 Apr 2026 08:57:57 +0000 |
| Retrieval time | 2026-04-28T09:14:15.361Z |
| Last seen | 2026-04-28T09:14:15.361Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
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| Cluster | baYExp8AruHz |
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| Publisher visit | Yes — open original |
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| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Computers and Society arXiv:2509.00462 (cs) [Submitted on 30 Aug 2025 (v1), last revised 9 Feb 2026 (this version, v3)] Title:AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights Authors:Jiannan Xu, Gujie Li, Jane Yi Jiang View a PDF of the paper titled AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights, by Jiannan Xu and 2 other authors View PDF HTML (experimental) Abstract:As artificial intelligence (AI) tools become widely adopted, large language models (LLMs) are increasingly involved on both sides of decision-making processes, ranging from hiring to content moderation.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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