Module 17 · Tier 6 — Critical Evaluation

AI Communication & Transparency

Attribution, disclosure, and professional integrity in an AI-assisted world.

10 min Tier 6 — Critical Evaluation

Why This Matters

Disclosure norms for AI are still forming. What's expected varies by industry, context, and organizational policy. But the practitioners who navigate this best aren't waiting for norms to settle — they've developed a principled framework for when they disclose AI use, how they communicate it, and where the line is between AI assistance and AI replacement. This module makes that framework explicit.

AI use isn't binary — it spans a spectrum from full replacement to light assistance:

Full replacement: AI generates the core content; human approves and sends. Highest disclosure obligation.

Heavy assistance: AI provides substantial draft; human restructures and edits significantly.

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The Concept

The Disclosure Spectrum

AI use isn't binary — it spans a spectrum from full replacement to light assistance:

Full replacement: AI generates the core content; human approves and sends. Highest disclosure obligation.

Heavy assistance: AI provides substantial draft; human restructures and edits significantly.

Moderate assistance: AI provides specific elements; human provides overall structure and judgment.

Light assistance: AI helps with research, editing, or specific phrases; human writes the substantive content.

Background tools: Spell check, grammar tools, research aggregators. Generally no disclosure expected.

Where on this spectrum disclosure becomes appropriate depends on context. Academic work and journalism have the strictest norms. Internal work has the most latitude. Professional services (consulting, legal, medical) are context-dependent and evolving.

The Three Disclosure Questions

Would the recipient feel misled if they knew? If someone paying for your expertise would feel deceived to learn AI wrote the substantive content, that's a disclosure signal — and possibly a reason to reconsider the use case entirely.
Does the context have an explicit policy? Academic institutions, publishers, and many employers now have explicit AI use policies. Know them and follow them.
Am I representing AI output as my own judgment? Sharing AI analysis labeled as your professional recommendation without disclosure misrepresents what the client is receiving.

How to Communicate AI Use

When disclosure is appropriate, how you communicate it matters:

Straightforward: "This summary was generated with AI assistance and reviewed by me for accuracy." Honest, professional, no apology.

Context-setting: "I used AI to synthesize the research — the analysis and recommendations are mine." Differentiates AI's role from your judgment.

Proactive: Tell clients or stakeholders your AI use policy before they ask. This frames AI as a quality tool, not a shortcut — and establishes trust before it's tested.

The Authenticity Standard

Beyond disclosure: professional integrity with AI comes down to whether the judgment, expertise, and accountability are genuinely yours. Using AI to help you think and write more effectively is authentic. Using AI to simulate expertise you don't have is not — and the consequences of the latter are ultimately reputational.

Proactive disclosure done well

A consultant includes this in their standard engagement terms:

"We use AI tools to assist with research synthesis, document drafting, and analysis — always with human expert review and accountability. All recommendations represent our professional judgment. We're happy to discuss our AI use practices in more detail if useful."

This sets expectations, frames AI as a quality tool, and positions the consultant as transparent and modern. It takes one paragraph to write and removes a potential trust problem before it arises.

Hands-On Exercise

Draft your personal AI disclosure approach

Any writing tool

Answer these questions in writing: 1. In your professional context, what types of AI use would you disclose, and what types are at the background-tool level? 2. Write a one-paragraph statement of your AI use approach — something you could include in a professional context or share with a client who asked. 3. Is there an explicit policy about AI use in your organization or industry? If yes, are there any gaps between what it says and what you currently do? 4. Is there anything you currently use AI for that you would feel uncomfortable disclosing if asked directly? If yes, that's your answer about whether to change that practice. The last question is the most important one. Answer it honestly.

Your answers to question 4 are more informative than your answers to question 1. Trust that discomfort.

Active Recall

Before moving on — close this lesson and answer these from memory. Then come back and check. Testing yourself (not re-reading) is how this sticks.

1 Describe the disclosure spectrum. Where do the clearest disclosure obligations sit? Where is the most latitude?

2 What are the three disclosure questions? Which one is the most fundamental, and why?

Reflection

Where in your current AI use is the gap between what you do and what you'd be comfortable disclosing? What would you need to change to close that gap?

Key Takeaway

AI disclosure depends on context, explicitness of policy, and whether you're representing AI output as your judgment. The authenticity standard: the judgment, expertise, and accountability must be genuinely yours. Proactive disclosure builds trust; reactive disclosure after a question erodes it.