Trust Calibration

Calibrated trust is the difference between an AI that augments user judgment and one that displaces it. Overtrust causes harm when the AI is wrong. Undertrust wastes the AI when it's right. Both failure modes are common, and neither shows up in standard accuracy metrics.

Designing for trust means giving users the information they need to update their trust appropriately, turn by turn.

What shapes user trust in the moment

• Surface confidence — how certain the AI sounds, regardless of whether it should
• Track record — prior interactions in this and previous sessions
• Stakes legibility — how clearly the user understands what could go wrong
• Source visibility — whether the AI shows reasoning, sources, or alternatives
• Persona fit — a "professional" persona gets more trust than a "friendly" one for the same content

These shape trust whether you design for them or not. Designing for them deliberately is what trust calibration is.

Trust failure modes