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OmniMechanism Design for Human-AI Collaboration with Impressionable Minds — arXiv:2611.00441

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00441
License (as archived): CC0-1.0
Archived: 2026-11-03T18:11:57.546Z
SHA-256 of the archived text: cac0c5dd87cb5af5355f93d4908ff22168ea69e8129b7fbd179182039e1550e1
Stable URL of this page: https://metraipolis.com/documento/dc_d10cd09247ec

How to cite

OmniMechanism Design for Human-AI Collaboration with Impressionable Minds — arXiv:2611.00441 — arXiv (en), https://arxiv.org/abs/2611.00441 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d10cd09247ec (Metraipolis archive dc_d10cd09247ec, sha256 cac0c5dd87cb5af5355f93d4908ff22168ea69e8129b7fbd179182039e1550e1)

BibTeX

@misc{dcd10cd09247ec,
  title = {OmniMechanism Design for Human-AI Collaboration with Impressionable Minds — arXiv:2611.00441},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00441},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d10cd09247ec; sha256 cac0c5dd87cb5af5355f93d4908ff22168ea69e8129b7fbd179182039e1550e1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00441. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

As AI systems are increasingly deployed in high-stakes domains such as healthcare, effectively integrating human expertise with AI outputs is critical for reliable decision-making. A substantial body of theoretical work studies optimal mechanisms for human-AI collaboration (HAC), focusing on when to involve humans and how to combine their expertise with AI predictions. However, these approaches largely overlook a key factor highlighted by empirical evidence: human judgments are impressionable and can vary depending on how AI outputs are presented. For example, when AI systems defer decisions to humans, revealing or withholding the AI prediction can significantly influence human judgments. Moreover, existing work neglects deployment-time variability, where conditions, particularly the cost of eliciting human input, may differ substantially from those observed during training. To address these challenges, we introduce a rigorous and theoretically grounded framework for human-AI collaboration, termed collaborative omnimechanism design, based on a novel and computationally efficient contextual bandit variant of boosting for outcome indistinguishability. In particular, we reduce a calibration-type outcome indistinguishability condition to weighted calibration, providing a novel time-efficiency result, bypassing the high-dimension curse induced by complicated collaboration methods and outcome space. Our framework provides a principled characterization of human impressionability and introduces a unified optimization scheme that enables robust and adaptable decision-making across diverse query costs and evaluation objectives without retraining. We evaluate the framework in three HAC settings, examining adaptation to query costs and prediction objectives without retraining, and show that its induced policies perform comparably to or better than the evaluated baselines.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Human-Computer Interaction (cs.HC); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.00441 — 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 2026-11-03T18:09:53.281Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).