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Trust and Transparency Patterns(TTP)
Design patterns for building user trust through explainable AI interfaces, decision transparency, and source attribution
Complexity: highUI/UX & Human-AI Interaction
In 30 seconds
- What
- Surfaces AI reasoning, data sources, and confidence levels through expandable explanations, visual indicators, and decision breakdowns users can inspect.
- When to use
- High-stakes decisions, regulated domains, or when users need to verify AI output before acting on recommendations or generated content.
- Watch out
- Over-explaining creates cognitive overload; users ignore detailed transparency if it's too dense or always visible by default.
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Trust and Transparency Patterns: Overview
Design patterns for building user trust through explainable AI interfaces, decision transparency, and source attribution
- Explainable AI interfaces with progressive disclosure
- Decision visualization and reasoning transparency
- Source attribution and citation systems
- Confidence indicators and uncertainty communication
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References
The papers, specifications, and repositories this pattern is based on.