Calibrated Trust
Trust in AI that roughly matches what a system can reliably do in a specific context.
Calibrated trust is trust that roughly matches what an AI system can reliably do in a specific context. It sits between blind acceptance and blanket rejection. The goal is not more trust by default, but better alignment between confidence, evidence, use case and human responsibility.
Why it matters
People can over-rely on AI when an answer feels fluent, fast or authoritative. They can also under-use helpful systems when failures, uncertainty or unclear design make the tool feel unreliable. Calibrated trust gives teams a way to ask what the system is good at, where it is fragile and what checks belong in the workflow.
In everyday work, this can mean deciding when AI output is a draft, when it is a suggestion, and when it needs independent verification before it influences a decision.
What not to simplify
Trust is not only an attitude or a feeling. It is shaped by task, stakes, feedback, prior experience, interface design and the user’s ability to notice limits. A confidence score or polished answer does not solve trust on its own.
Sources and context
- Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50-80. https://doi.org/10.1518/hfes.46.1.50_30392
- National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce. https://www.nist.gov/itl/ai-risk-management-framework
- Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230-253. https://doi.org/10.1518/001872097778543886
Related reading
Continue exploring
- TopicTrust in AI
A guide to sources, uncertainty, verification and calibrated reliance.
- ArticleTrust in the age of ready-made answers
A research-informed essay on what disappears when a fluent answer compresses sources, uncertainty, disagreement and responsibility.
- ConceptEpistemic vigilance
How people assess communicated information and the sources behind it.
- ConceptGrounding
Connecting an answer to material that can be inspected and evaluated.
- PracticeHow to check whether an AI answer has sources
A repeatable source check for claims that arrive without a visible evidence trail.
© 2026 Feliks Mamczur / Prompted Psyche. All rights reserved. Short quotations are permitted only with attribution to the author, the title and a link to the source.