Model Disclosure Requirements

What must a lab tell the public or regulators about a model before shipping it? The answer used to be 'nothing.' It is becoming more.

38 min · Reviewed 2026

The Transparency Gap

Until ~2023, what a lab disclosed about a new model was entirely voluntary. You got a blog post, maybe a technical report. Weights, training data, evaluation methodology, and safety testing were proprietary. That norm is changing, unevenly.

Formal requirements (as of ~2025-2026)

EU AI Act: General-purpose model providers must publish detailed documentation and copyright-policy summaries; systemic-risk models face additional obligations including evaluations and incident reporting
UK AI Safety Institute pre-deployment evaluation access (voluntary but expected)
California SB 53 (signed 2025): transparency reports from frontier developers on safety frameworks
White House voluntary commitments (2023): red-teaming disclosures and content-provenance commitments from major labs

What's still mostly opaque

Exact training data composition — most labs share only high-level descriptions
Red-team findings — summarized at best, rarely reproducible
Copyright-relevant data sources — a subject of active litigation
Fine-tuning and alignment dataset specifics
Deployment monitoring data

Companies that publish the most detailed safety information tend to be the ones doing the most safety work. That correlation could reverse if disclosure becomes mandatory and checkbox-shaped.
— Rishi Bommasani, Stanford CRFM (paraphrased)

The big idea: disclosure is the first step toward accountability. Accountability requires someone qualified to read the disclosure and act on it. Both are still under construction.

End-of-lesson check

8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-safety2-model-disclosure-creators

What is the main idea of "Model Disclosure Requirements"?
1. What must a lab tell the public or regulators about a model before shipping it? The answer used to be 'nothing.' It is becoming more.
2. Use AI as the final authority for the whole decision
3. Avoid checking the answer once it sounds polished
4. Focus only on speed instead of judgment
Which concept is most central to "Model Disclosure Requirements"?
1. transparency
2. model card
3. disclosure
4. system card
Which use of AI fits this topic best?
1. Let the AI decide what matters without your review
2. Use the answer before checking whether it fits the situation
3. EU AI Act: General-purpose model providers must publish detailed documentation and copyright-policy summaries; systemic-risk models face additional obligations.
4. Treat the AI output as automatically correct
What should a careful learner remember about "Model cards and system cards"?
1. Use "Model cards and system cards" as a reminder to verify the AI output before anyone relies on it.
2. Skip the context so the tool can guess faster
3. Treat the output as private even after sharing it online
4. Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
1. Act immediately because the AI answer is written clearly
2. AI cannot make the human values decision for you.
3. Hide uncertainty so the final answer looks cleaner
4. Use private or sensitive details before checking permission
How should AI output about model card be treated?
1. As proof that no other source is needed
2. As a replacement for context, consent, or expert review
3. As a draft or helper output that still needs human judgment and verification
4. As something that becomes correct when it sounds confident
Name one way to verify an AI answer about model card.
Which action would help you apply "Model Disclosure Requirements" responsibly?
1. Use the tool to avoid thinking through the tradeoff
2. Keep going even if the output conflicts with a trusted source
3. Treat the AI output as automatically correct
4. UK AI Safety Institute pre-deployment evaluation access (voluntary but expected)

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