Lesson 982 of 2116
AI Attribution Norms: When and How to Disclose AI Involvement in Your Work
Disclosure norms for AI involvement are forming in real time across industries. Erring toward over-disclosure protects credibility; under-disclosure produces avoidable trust failures.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2AI attribution
- 3disclosure
- 4transparency
Concept cluster
Terms to connect while reading
Section 1
The premise
AI attribution norms are forming; over-disclosure is safer than under-disclosure for professional credibility.
What AI does well here
- Develop a personal disclosure standard you apply consistently
- Distinguish AI-assisted (you led the work) vs AI-generated (AI led the work)
- Disclose where AI contributed substantively to the work product
- Document your contribution for any work you'll claim authorship of
What AI cannot do
- Substitute disclosure for the work itself (disclosure doesn't excuse poor quality)
- Predict every emerging norm (the landscape is shifting)
- Avoid the credibility cost when disclosure was warranted but skipped
Key terms in this lesson
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
Curious about “AI Attribution Norms: When and How to Disclose AI Involvement in Your Work”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Creators · 40 min
AI customer-facing AI use disclosure pattern library
Use AI to draft a library of disclosure patterns for customer-facing AI use across product surfaces.
Creators · 40 min
AI and an AI-use disclosure template
Use AI to draft a disclosure block readers can trust, naming what AI did and didn't do in your work.
Explorers · 40 min
Share AI Stuff Honestly: It Builds Trust
When you share something AI helped you make, telling people is honest and builds trust. Hiding it makes you look bad later.