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Evaluative Dynamics of AI Integration and Expert Performance under Epistemic Dependence across Heterogeneous Stakes — arXiv:2609.23135

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.23135
License (as archived): CC0-1.0
Archived: 2026-10-09T07:01:50.872Z
SHA-256 of the archived text: 1d12ee85d4cdb1294ea992f11dfa4bf84d29da4ca8139d8c86ecc9dcb00db92a
Stable URL of this page: https://metraipolis.com/documento/dc_ac5df373fcbf

How to cite

Evaluative Dynamics of AI Integration and Expert Performance under Epistemic Dependence across Heterogeneous Stakes — arXiv:2609.23135 — arXiv (en), https://arxiv.org/abs/2609.23135 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ac5df373fcbf (Metraipolis archive dc_ac5df373fcbf, sha256 1d12ee85d4cdb1294ea992f11dfa4bf84d29da4ca8139d8c86ecc9dcb00db92a)

BibTeX

@misc{dcac5df373fcbf,
  title = {Evaluative Dynamics of AI Integration and Expert Performance under Epistemic Dependence across Heterogeneous Stakes — arXiv:2609.23135},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.23135},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ac5df373fcbf; sha256 1d12ee85d4cdb1294ea992f11dfa4bf84d29da4ca8139d8c86ecc9dcb00db92a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.23135. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

AI is increasingly integrated into expert workflows, yet how integration affects perceptions of the expert, AI, and their combination remains unclear in domains where lay users are epistemically dependent on AI-assisted experts. We examine this through a novel controlled medical study (N = 166) and a direct cross-domain analysis with pre-existing academic-advising data (n = 157, combined N = 323). Expert errors reduced evaluations of the human expert across domains. Perceived expertise, however, varied by AI integration strategy in the higher-stakes medical task, where automatic AI oversight produced higher ratings than expert-only or expert-initiated AI. Exploratory ordinal sensitivity analyses identified a performance-contingent reuse pattern, with automatic oversight producing greater intended reuse after successful medical performance. Overall, performance-related recalibration appeared comparatively portable, while integration-structure effects were more selective and context-sensitive. These findings suggest that system designers should consider how AI enters expert workflows, not only whether it is present.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-22 in cs.HC).
Categorie arXiv: Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2609.23135 — 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 2026-10-09T06:56:29.218Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).