To judge an AI company’s safety disclosures, look for two different things: a dated policy explaining who makes risk decisions and what actions follow, and model-specific evaluation reports showing what was tested, how, and with what limitations. Treat both as evidence to inspect—not proof that a system is safe in every real-world use.
Start by separating policy from evidence
A safety policy or framework describes intended governance: the risks a company considers, who is responsible, and how it says it will respond. An evaluation report describes evidence about a particular model or system under stated test conditions. A policy commitment is not a test result; a test result is not a guarantee of deployment safety.
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Company-authored disclosures can be informative when they provide enough detail to check their claims, but readers generally cannot reproduce an evaluation or infer full operational performance from a short report. Keep the source and limits of each claim in view.
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Find a dated public policy or framework, then check whether it gives operational detail rather than broad principles alone.
#1 Best Overall
- Scope: Which systems, capabilities, uses, and risks does it cover? Is the framework intended for frontier models, a particular product, or the company’s AI work more broadly?
- Governance: Who assesses risk, who has decision authority, and who can delay or restrict a release? A named council or team is useful context, but it does not substitute for model-level evidence.
- Decision criteria: Does the policy identify thresholds or other criteria that trigger action? Look for a connection between a risk finding and a defined decision, not just a promise to consider risks.
- Mitigation and response: What safeguards, release restrictions, or other mitigations are required? Does the policy explain how incidents are handled?
- Updates: Is the policy dated and versioned? Does the company explain how it will revise the framework as capabilities, evaluations, or requirements change?
NIST’s AI Risk Management Framework can provide a reference vocabulary for risk management, but it is voluntary guidance, not a certification that a company or model is safe. NIST says AI RMF 1.0 is being revised, so check the current edition rather than assuming the 2023 version is settled. See the NIST AI Risk Management Framework.
For examples of what governance disclosures can look like, Google DeepMind describes a Responsibility and Safety Council, an AGI Safety Council, and a Frontier Safety Framework for possible severe risks from powerful frontier models on its responsibility and safety page. Such descriptions help identify governance structures to examine; look separately for public evaluation results tied to specific systems.
Rank #2
Inspect the evaluation report for a specific model
Search for a system card, model card, or evaluation report tied to the model and version under discussion. A general safety page may explain the company’s approach, but it cannot tell you what a particular release was tested against.
- Identity and date: Does the report name the model or system version and say when testing or publication occurred?
- Risks and capabilities: What hazards, misuse cases, or capabilities did the evaluation cover? What was outside its scope?
- Method and metric: Does the report describe the test setup, measurement, and relevant baseline? Are results understandable rather than just asserted?
- Conditions: Were tests run offline, with deliberately challenging prompts, or under another defined setup? Could those conditions differ from actual product use?
- Results and limitations: Does the company report outcomes, uncertainty, known weaknesses, and gaps in coverage—not only favorable findings?
- Changes after findings: Does the report say whether test results led to mitigations, release decisions, or further work?
Anthropic’s Transparency Hub describes model or system cards covering capabilities, benchmark performance, known limitations and risks, safety evaluations and red-team results, and training information. It also lists the company’s Responsible Scaling Policy and Frontier Compliance Framework. Treat these as document categories to inspect, not independent confirmation of the claims.
Rank #3
OpenAI’s GPT-4o System Card is another concrete example. OpenAI reports that it worked with more than 100 external red teamers, speaking 45 languages and representing 29 countries. Those counts describe participation as reported by OpenAI; they do not by themselves establish the quality of the testing or prove safety.
Read scores with their test conditions
A score is not a general safety rating. Its meaning depends on the tested model version, task, metric, data, evaluation pipeline, and test conditions. Even two scores for the same model family may not be comparable if any of those changed.
Rank #4
OpenAI’s GPT-5.5 System Card says its challenging-prompt error rates are “not representative of average production traffic.” The card also notes that evaluation scores may vary with data and pipelines. That is a useful reminder to read the caveat beside the number, rather than presenting a result as a measure of everyday product behavior.
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Before comparing companies or versions, check that the model versions, tasks, metrics, test conditions, and relevant baselines are demonstrably comparable. If the report does not establish that, record the results separately instead of ranking them. A missing detail is unknown; it is not evidence that the model passed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Assess outside review and after-release accountability
External red teaming, expert input, and an independent audit are not interchangeable. A report that says outside people participated is useful disclosure, but readers need more information to judge the strength of that review.
- Who conducted the review, and what expertise or relationship to the company is disclosed?
- What access did reviewers have—to the model, tools, data, or internal information?
- Which risks and systems were in scope, and what findings were published?
- What did the company change or decide in response?
OpenAI’s 2024 GPT-4o card reports external red-team participation; that is evidence of participation, not proof of a fully independent audit. A claim of external testing is most useful when the report makes the reviewer’s independence, access, scope, findings, and the company’s response visible.
Then look for a process that continues after release: monitoring for misuse or newly observed risks, a channel for incident reports, response procedures, and policy or safeguard updates. OpenAI’s Frontier Risk Framework describes post-release monitoring and iterative response. Its May 28, 2026 Frontier Governance Framework announcement lists risk assessment and mitigation, model reporting, security risk management, incident response, external expert input, and updates; this is OpenAI’s description of its own framework, not an independent assessment.
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For each company or model, record the same fields. This keeps a polished general policy from being mistaken for model-specific results, and makes missing information visible.
| Field | What to record |
|---|---|
| Policy | Document name, publication or revision date, scope, and decision owners |
| Model | System name and exact version evaluated |
| Risks tested | Evaluation categories and material risks not covered |
| Method and metric | Test setup, measurement, baseline, and conditions |
| Result and caveat | Reported findings, limitations, and whether the setting represents deployment |
| Outside review | Reviewer, independence, access, scope, findings, and company response |
| After release | Monitoring, incident reporting and response, and update process |
| Open questions | Details not stated in the public documents; leave them unknown rather than assuming a favorable result |
Use this record to compare the quality and checkability of what companies publish, not to declare one system safe on the basis of disclosure volume or a single benchmark.
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