Showing users what the AI knows, doesn't know, and how confident it is.
---
name: transparency-patterns
description: Showing users what the AI knows, doesn't know, and how confident it is.
---
# Transparency Patterns
Transparency in AI products means making the system's knowledge, limitations, and confidence visible to users. It's how you build warranted trust — trust based on understanding, not blind faith.
## What to Make Transparent
- **Source**: Where did the AI get this information? Training data, retrieved documents, user input, inference?
- **Confidence**: How certain is the AI? Is this a well-supported answer or a best guess?
- **Limitations**: What doesn't the AI know? What can't it do? Where does its knowledge end?
- **Process**: How did the AI arrive at this output? What steps did it take?
- **Identity**: This is an AI, not a human. Never obscure this.
## Transparency Patterns
- **Confidence indicators**: Visual or textual signals of certainty ("I'm fairly confident" vs. "I'm not sure about this")
- **Source attribution**: Citing where information came from
- **Reasoning traces**: Showing the AI's step-by-step thinking
- **Limitation disclosure**: Proactively stating what the AI can't do or doesn't know
- **Model cards**: High-level descriptions of what the AI is, how it works, and what it's good and bad at
- **Uncertainty highlighting**: Visually distinguishing confident outputs from uncertain ones
## Calibrating Transparency
Too much transparency overwhelms. Too little erodes trust. Calibrate by:
- **User expertise**: Experts want more detail. Novices want simple signals.
- **Task stakes**: High-stakes decisions need full transparency. Low-stakes interactions need less.
- **Output confidence**: Show more transparency when the AI is uncertain, less when it's confident.
- **User request**: Let users drill into details on demand rather than showing everything upfront.
## Transparency Anti-Patterns
- **Performative transparency**: Showing a reasoning trace that doesn't actually explain the decision
- **Buried disclaimers**: Putting limitations in fine print nobody reads
- **False confidence**: The AI sounds certain when it's guessing
- **Opaque refusal**: "I can't help with that" with no explanation
- **Transparency theatre**: Making the system look transparent without actually being informative
## Design Artefacts
- Transparency level specifications per feature
- Confidence communication guidelines
- Source attribution patterns
- Limitation disclosure templates
---
name: transparency-patterns
description: Showing users what the AI knows, doesn't know, and how confident it is.
---
# Transparency Patterns
Transparency in AI products means making the system's knowledge, limitations, and confidence visible to users. It's how you build warranted trust — trust based on understanding, not blind faith.
## What to Make Transparent
- **Source**: Where did the AI get this information? Training data, retrieved documents, user input, inference?
- **Confidence**: How certain is the AI? Is this a well-supported answer or a best guess?
- **Limitations**: What doesn't the AI know? What can't it do? Where does its knowledge end?
- **Process**: How did the AI arrive at this output? What steps did it take?
- **Identity**: This is an AI, not a human. Never obscure this.
## Transparency Patterns
- **Confidence indicators**: Visual or textual signals of certainty ("I'm fairly confident" vs. "I'm not sure about this")
- **Source attribution**: Citing where information came from
- **Reasoning traces**: Showing the AI's step-by-step thinking
- **Limitation disclosure**: Proactively stating what the AI can't do or doesn't know
- **Model cards**: High-level descriptions of what the AI is, how it works, and what it's good and bad at
- **Uncertainty highlighting**: Visually distinguishing confident outputs from uncertain ones
## Calibrating Transparency
Too much transparency overwhelms. Too little erodes trust. Calibrate by:
- **User expertise**: Experts want more detail. Novices want simple signals.
- **Task stakes**: High-stakes decisions need full transparency. Low-stakes interactions need less.
- **Output confidence**: Show more transparency when the AI is uncertain, less when it's confident.
- **User request**: Let users drill into details on demand rather than showing everything upfront.
## Transparency Anti-Patterns
- **Performative transparency**: Showing a reasoning trace that doesn't actually explain the decision
- **Buried disclaimers**: Putting limitations in fine print nobody reads
- **False confidence**: The AI sounds certain when it's guessing
- **Opaque refusal**: "I can't help with that" with no explanation
- **Transparency theatre**: Making the system look transparent without actually being informative
## Design Artefacts
- Transparency level specifications per feature
- Confidence communication guidelines
- Source attribution patterns
- Limitation disclosure templates
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.