LLM, 자신이 작성한 이력서를 인간보다 선호해

--> 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. This dual adoption raises a critical question: do LLMs systematically favor content that resembles their own outputs? Prior research in computer science has identified self-preference bias -- the tendency of LLMs to favor their own generated content -- but its real-world implications have not been empirically evaluated. We focus on the hiring context, where job applicants often rely on LLMs to refine resumes, while employers deploy them to screen those same resumes. Using a large-scale controlled resume correspondence experiment, we find that LLMs consistently prefer resumes generated by themselves over those written by humans or produced by alternative models, even when content quality is controlled. The bias against human-written resumes is particularly substantial, with self-preference bias ranging from 67% to 82% across major commercial and open-source models. To assess labor market impact, we simulate realistic hiring pipelines across 24 occupations. These simulations show that candidates using the same LLM as the evaluator are 23% to 60% more likely to be shortlisted than equally qualified applicants submitting human-written resumes, with the largest disadvantages observed in business-related fields such as sales and accounting. We further demonstrate that this bias can be reduced by more than 50% through simple interventions targeting LLMs' self-recognition capabilities. These findings highlight an emerging but previously overlooked risk in AI-assisted decision making and call for expanded frameworks of AI fairness that address not only demographic-based disparities, but also biases in AI-AI interactions. Comments: This paper has been accepted as a non-archival submission at EAAMO 2025 and AIES 2025 Subjects: Computers and Society (cs.CY) Cite as: arXiv:2509.00462 [cs.CY] (or arXiv:2509.00462v3 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2509.00462 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jiannan Xu [ view email ] [v1] Sat, 30 Aug 2025 11:40:11 UTC (3,032 KB) [v2] Thu, 11 Sep 2025 16:59:36 UTC (3,032 KB) [v3] Mon, 9 Feb 2026 13:24:26 UTC (5,723 KB) Full-text links: Access Paper: 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) TeX Source view license Current browse context: cs.CY < prev | next > new | recent | 2025-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )