The Geometry of Trust in Human–AI Collaboration
DOI:
https://doi.org/10.0000/meridian.demo.2026.001Keywords:
human-AI collaboration, trust, verificationAbstract
Trust in AI-assisted work is often treated as a single attitude. We instead model it as a structured relationship between uncertainty, verification cost, domain expertise, and institutional accountability. A mixed-method study of collaborative writing tasks reveals distinct trust profiles that predict when users verify, defer, or reject model suggestions.
References
Rossi, S., & Kim, D. (2025). Verification cost in collaborative AI systems. Journal of Synthetic Research, 4(2), 11–29.
Vidal, M. (2024). Trust calibration across multilingual interfaces. Proceedings of the Demo Systems Symposium, 88–101.
Moreau, C. (2025). Institutional accountability as a model constraint. Computational Governance Review, 9(1), 1–18.
Published
2026-06-18
Issue
Section
Articles
