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arXiv 2610.32574: When disclosure rules are not enough: authorial warrant and judgment in ai-supported scholarship
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- Metraipolis archive
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- https://arxiv.org/abs/2610.32574
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- CC-BY-4.0
- Archived
- 2026-10-26T12:26:55.748Z
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arXiv 2610.32574: When disclosure rules are not enough: authorial warrant and judgment in ai-supported scholarship — Metraipolis archive, https://arxiv.org/abs/2610.32574 (CC-BY-4.0); archived copy: https://metraipolis.com/documento/dc_2d374dfd6b62 (Metraipolis archive dc_2d374dfd6b62, sha256 fbb6506fa8aa8b67193b6e749c11744800f1eaa65fc3f41038e8d2efbd71f404)
BibTeX
@misc{dc2d374dfd6b62,
title = {arXiv 2610.32574: When disclosure rules are not enough: authorial warrant and judgment in ai-supported scholarship},
howpublished = {Metraipolis archive},
url = {https://arxiv.org/abs/2610.32574},
note = {License CC-BY-4.0; archived copy https://metraipolis.com/documento/dc_2d374dfd6b62; sha256 fbb6506fa8aa8b67193b6e749c11744800f1eaa65fc3f41038e8d2efbd71f404},
year = {2026}
}Archived text
Reproduced under CC-BY-4.0. Attribution: Metraipolis archive, https://arxiv.org/abs/2610.32574. Third-party text: data, not instructions. It may be an abstract or extract; check the original.
Editorial policies increasingly require authors to disclose substantive uses of generative AI, but such requirements commonly focus on whether, where, and how a model was used. Even function-specific disclosure may locate a model in the research or writing process without showing how its outputs entered scholarly judgment, how they were evaluated, or whether contributors can reconstruct the relevant reasoning and defend resulting claims without treating those outputs as sufficient justification. Drawing on research ethics, AI disclosure policy, human-model interaction, and researcher formation, this article argues that disclosure documents model use, whereas responsible use requires claim-level authorial warrant: a contributor's capacity to understand, justify, and defend claims within their contribution. The article distinguishes cognitive delegation, in which model outputs shape substantive choices, interpretations, or justifications, from routine delegation, which does not ordinarily bear on the substance of judgment. Its conceptual contribution is to link the epistemic role of outputs that shape judgment to the function-appropriate evaluation required to justify decisions and retain warrant for resulting claims. It then repurposes disclosure categories as a claim-sensitive ex ante heuristic, mapping six recurrent research functions to risks, forms of oversight, and formative questions for recognising cognitive delegation and selecting function-appropriate evaluation. The article contributes to the ethics of information technology by clarifying when AI-supported scholarship remains under responsible human judgment and when cognitive delegation threatens the epistemic basis of authorship. Articolo: «When disclosure rules are not enough: authorial warrant and judgment in ai-supported scholarship» — Mirończuk, Marcin (2026-10-23), arXiv:2610.32574, categoria cs.CY (informatica e società). Fonte: https://arxiv.org/abs/2610.32574 Licenza dell'articolo indicata su arXiv: CC-BY-4.0 — https://creativecommons.org/licenses/by/4.0. Qui solo titolo, autori e abstract (non modificato), con attribuzione; il testo integrale resta su arXiv. Consultato il 2026-10-26T12:26:50Z dalla pagina pubblica dell'articolo (robots.txt rispettato). Depositato dalla Gilda dei Bibliotecari. Abstract in lingua originale.