AI Safety Cases vs Checklists: Which Fits Your Needs?

Updated 7 November 2026 · Standard formula, shown below

AI safety cases and checklists are two distinct approaches to ensuring AI systems are safe. A safety case is a structured, evidence-based argument that a system is acceptably safe for a specific context, while a checklist is a simple list of required actions or criteria. The right choice depends on your project’s stage, risk level, and regulatory environment—often a hybrid is best.

Background: Two Philosophies of Safety Assurance

Safety cases come from industries like aviation and nuclear power, where failures can be catastrophic. They were formalized in the 1990s with standards like the UK’s Defence Standard 00-56. A safety case is a living document that argues, with evidence, why a system is safe to operate in a given environment. It includes a claim (e.g., “the system will not cause harm”), argument (logical reasoning linking evidence to the claim), and evidence (test results, analyses, expert reviews). Checklists, on the other hand, are much older—think of pre-flight checklists in aviation. They are simple, actionable lists of items to verify or tasks to complete. In AI, checklists like the ML Test Score or the Responsible AI Checklist provide a quick way to ensure common risks are considered.

How Each Approach Works

Safety cases are built through a structured process:

Define the system’s operational context and safety requirements.
Identify hazards and estimate risk.
Construct a safety argument using a notation like Goal Structuring Notation (GSN).
Collect evidence—testing, formal verification, monitoring data, expert judgment.
Maintain and update the case as the system or environment changes.

Checklists are simpler: you list items like “test for bias on protected attributes” or “verify model version matches deployment manifest,” then mark them off. They are often derived from best practices or past incidents. For example, Google’s PAIR checklist includes “Check for fairness” and “Document model version.”

When to Use a Safety Case

Use a safety case when the AI system has high potential for harm, such as in autonomous vehicles, medical diagnostics, or critical infrastructure. It is also appropriate when regulators expect a formal assurance argument (e.g., EU AI Act for high-risk systems). Safety cases are valuable for complex systems where safety emerges from interaction of components, not just individual parts. They force you to think deeply about what “safe” means for your specific use case and to justify why your evidence is sufficient.

When to Use a Checklist

Checklists are ideal for lower-risk applications, early development stages, or as a complement to a safety case. They are quick to implement, easy to audit, and help ensure consistency across teams. For example, before deploying a recommendation system, a checklist might include “evaluate for filter bubbles” or “check for data leakage.” Checklists are also useful for routine tasks like “run unit tests” or “review model cards.” They are not sufficient for high-risk systems because they don’t provide a coherent argument—they just list actions without explaining why those actions guarantee safety.

Common Problems and Misconceptions

A common misconception is that a checklist is a safety case. It is not. A checklist can be part of the evidence, but it doesn’t argue for safety. Another problem is that safety cases can become bureaucratic and stale if not updated. They require significant expertise to create and maintain. Checklists can give a false sense of security if items are checked off without proper verification. For instance, “test for bias” might be checked after a single test that doesn’t cover all relevant subgroups. Also, both approaches suffer from the “unknown unknowns”—you can’t check or argue for risks you haven’t imagined.

How to Choose: A Practical Framework

Consider three factors: risk level, regulatory pressure, and team capabilities.

If the system is low-risk (e.g., a content filter), a checklist may suffice.
If it is high-risk (e.g., autonomous driving), a safety case is necessary.
If regulators require a safety case, you must build one, but you can use checklists to organize evidence.
If your team lacks safety engineering expertise, start with a checklist and gradually build a safety case as you learn.

In practice, many organizations use both: a high-level safety case that references checklists for specific activities. For example, a safety case for a medical AI might include a checklist for data validation as evidence.

Costs and Trade-offs

Safety cases are expensive—they can consume 10-30% of project budget in safety-critical industries. They require specialized staff, time for documentation, and ongoing maintenance. Checklists are cheap—essentially the cost of writing and reviewing a list. However, the cost of not having a safety case can be much higher if an accident occurs. The trade-off is between upfront investment and potential liability. For startups with limited resources, a checklist might be the only feasible option, but they should be aware of the limitations.

Alternatives and Complements

Other approaches include formal verification (mathematically proving properties), runtime monitoring (detecting unsafe behavior in real time), and red-teaming (adversarial testing). These can complement both safety cases and checklists. For instance, a safety case might include formal verification results as evidence, and a checklist might include a red-team exercise. The key is to choose a combination that provides adequate assurance for your specific risk profile.

Frequently Asked Questions

Can a checklist replace a safety case for a high-risk AI system? No. A checklist lacks the argumentative structure that explains why the listed items are sufficient for safety. Regulators and courts will expect a reasoned case, not just a list.

How do I start building a safety case for my AI? Begin with a hazard analysis, then define safety requirements. Use GSN to structure your argument. Collect evidence as you develop and test. Iterate as you learn.

Are there tools to help manage safety cases? Yes, there are tools like Adelard’s ASCE and the open-source GSN tool, but they are not required. A well-organized document repository can work.

What is the biggest mistake in using checklists? Treating them as a substitute for critical thinking. Checklists should prompt review, not replace it. Always ask “why” each item is on the list.

How do safety cases handle AI’s non-determinism? They rely on statistical evidence, such as testing over many scenarios, and on design features like uncertainty quantification. The argument must justify that the evidence covers the operational envelope.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.