Trust Calibration in AI

Definition

Trust Calibration in AI examines the mechanisms, conditions and outcomes through which this factor shapes human cognition, learning, collaboration or sustainable performance in AI-supported settings.

Why this matters

Trust Calibration in AI may materially affect human capability, independence, confidence, safety and productivity. Poorly designed assistance can create hidden costs even where short-term output appears to improve.

Research questions

How can user trust be aligned with actual system capability and reliability?

Hypotheses

Outcome histories with failure examples will calibrate trust better than confidence labels alone.

Proposed methods

workflow experiments; task-allocation trials; simulation; incident analysis; ethnography; decision audits; controlled handover tests; pre-registered analysis; active comparison; subgroup and accessibility analysis; delayed retention or longitudinal follow-up; adverse-effect capture; reproducibility testing; participant debrief

Stakeholders and beneficiaries

workers; team leaders; boards; risk officers; AI vendors; unions; customers; regulators; auditors; assurance teams; customer trust leaders