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AI evaluators favor job applications written by certain AI models, independent of candidate quality — arXiv:2610.41866

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41866
License (as archived): CC0-1.0
Archived: 2026-11-03T07:29:17.744Z
SHA-256 of the archived text: 13dad947ac0717dc7ebec1c42bde1965546013f0b5ea0cb36d3462c43e0e54c7
Stable URL of this page: https://metraipolis.com/documento/dc_0dc2bf75df90

How to cite

AI evaluators favor job applications written by certain AI models, independent of candidate quality — arXiv:2610.41866 — arXiv (en), https://arxiv.org/abs/2610.41866 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0dc2bf75df90 (Metraipolis archive dc_0dc2bf75df90, sha256 13dad947ac0717dc7ebec1c42bde1965546013f0b5ea0cb36d3462c43e0e54c7)

BibTeX

@misc{dc0dc2bf75df90,
  title = {AI evaluators favor job applications written by certain AI models, independent of candidate quality — arXiv:2610.41866},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41866},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0dc2bf75df90; sha256 13dad947ac0717dc7ebec1c42bde1965546013f0b5ea0cb36d3462c43e0e54c7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41866. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Job applicants increasingly use large language models (LLMs) to write cover letters, while employers deploy LLMs to screen them. In this all-AI setting, we ask whether the \emph{choice of model} affects hiring outcomes independently of candidate qualifications. We introduce a multi-model auditing framework pairing eleven LLM writer agents with nine API-hosted evaluator agents across ten job descriptions and fifty candidates per job. Four findings emerge. First, we identify a generative preference hierarchy: a consistent global ranking of writer models by evaluator acceptance, in which locally hosted models are systematically disadvantaged regardless of their license, compounded by self-preference bias where evaluators favour their own outputs. Second, evaluator models differ substantially in how much bias they introduce based on applicants' tool choice, offering actionable guidance for fairer screening design. Third, this hierarchy has measurable competitive consequences: in the most adverse pairing, approximately two (1.8) out of twenty-five qualified candidates are filtered out purely due to model mismatch. Fourth, a common mechanism explains the patterns above: writer preference and self-preference trace to a combination of how much each writer leans on specific stylistic cues and how strongly evaluators, often unanimously, reward or penalise those same cues. A further check finds little evidence that an unmeasured "writer fingerprint" remains once these same features are accounted for. We discuss implications for algorithmic fairness in employment, regulation of AI-assisted screening, and the design of auditing frameworks that can be reapplied as the model landscape evolves.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Computers and Society (cs.CY) ; Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2610.41866 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T07:29:17.664Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).