ux-research checked
People + AI Guidebook: Explainability and Trust
Documents trust calibration, probability and uncertainty, partial explanations, system explanations, output explanations, confidence displays, and when users should apply judgment.
Pattern Decisions This Source Supports
| Pattern | Supported decision | Required contract | Claim note |
|---|---|---|---|
| AI confidence shown as fake precision | Flag this anti-pattern when an AI or automated surface shows a precise score without calibration scope, decision threshold, freshness, uncertainty reason, or safe next action. | The interface distinguishes raw internal scores from user-facing calibrated confidence. | Supports trust calibration, probability and uncertainty, confidence displays, and user judgment around AI output. |
| AI limitation onboarding | Choose AI limitation onboarding when the user needs first-use or changed-capability orientation about AI benefits, limits, data scope, uncertainty, feedback, human responsibility, and fallback. | The onboarding explains what the AI can help with and what it is not able to do before the first risky use. | Supports trust calibration, uncertainty, explanations, and when users should apply judgment. |
Evidence Role
This source is treated as ux-research evidence. Use it to validate the decision rules above, not as a visual style reference.
Publisher: Google PAIR. Last checked: .