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transparency-patterns

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

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/Owl-Listener/ai-design-skills/blob/HEAD/claude-plugin/ai-alignment-reasoning/skills/transparency-patterns/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/transparency-patterns/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

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