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transparency-patterns

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Showing users what the AI knows, doesn't know, and how confident it is.

skills/ai-alignment-reasoning/transparency-patterns/SKILL.md

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# 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
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