trust-calibration

You can calibrate user trust by applying confidence signaling, source attribution, and explicit hedging rules. This involves using language like "I don't know" or "According to X" to reveal AI limitations and provenance, preventing both overtrust and undertrust.

Confidence signaling in human-AI interaction is the practice of using explicit language to indicate an AI's epistemic state. It helps users decide when to trust an answer by presenting alternatives and transparently revealing past corrections and uncertainties.

To design transparent source attribution guidelines, establish rules for citing sources and surfacing alternative interpretations. This improves transparency by showing provenance, allowing users to verify critical steps during product decisions or design reviews.

You need failure transparency for AI-generated explanations when users must understand model uncertainties and handle edge cases. It prevents overtrust by revealing past corrections and how the model behaves when it lacks sufficient information.

The best way to prevent overtrust and undertrust is to present reasoning alongside sources and explicit confidence levels. By utilizing alternative interpretations and explicit hedging rules, users can independently verify critical steps and limitations.