CONCEPT
Trust Calibration (Klein)
Klein's framework for appropriate reliance on AI — not more trust or less trust, but trust calibrated to the system's actual performance in the specific situation at hand.
Trust calibration is Klein's alternative to the institutional framing of human-AI interaction as a binary trust problem. The standard framing treats resistance to AI as something to be overcome — users must learn to trust the system. Klein's framework rejects this framing. The goal is neither more nor less trust but appropriate trust: trust that matches the system's demonstrated competence in the specific situation at hand. Calibrated trust requires the user to have a mental model of the system's competence — an understanding of where it performs well, where it fails, and the boundary between. Building this mental model requires experience with the system's behavior across a range of conditions, including, critically, conditions under which the system fails. The user who has seen the system fail has the experiential foundation for calibration. The user who has only seen success has no basis for calibration and defaults to either wholesale trust or wholesale distrust.
In The You On AI Field Guide
The framework emerged from Klein's