ux-research 2 linked patterns 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.
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What this source can teach
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- Start with the original. Open the source and find the guidance or example relevant to your task.
- Trace the connection. Use the claim notes below to see how this site applies the source to specific patterns.
- Check the setting. Compare the source's platform, people, and constraints with your own decision.
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. |
Comparisons supported by this source
- AI confidence shown as fake precision vs confidence / uncertainty display vs meter vs source grounding display vs warning text vs human approval gate
Supports trust calibration, probability and uncertainty, confidence displays, and user judgment.
- AI limitation onboarding vs onboarding vs inline message vs warning text vs confidence uncertainty display vs source grounding display vs scope clarification vs human approval gate
Supports trust calibration, uncertainty, partial explanations, and user judgment.
Evidence Role
This source is treated as ux-research evidence. Its value comes from the specific claims and setting described above.
Publisher: Google PAIR. Last checked: .