The Governance Layer: Building a Formal AI Safety Case
Explore how to develop a formal AI safety case that proves your AI system's safety and ethical acceptability using the BIG argument framework. Learn to structure safety arguments across ethics, system architecture, and model layers with evidence from technical evaluations. This lesson helps you understand how to shift from technical fixes to a comprehensive, auditable assurance system aligned with real-world governance and regulatory needs.
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We have now concluded the technical, hands-on section of this course. We can break a model (PGD), audit its decisions (SHAP), align its intent (RLHF), and test its resilience (PyRIT). But in the world of production and regulation, knowing your system is safe internally is not enough.
You must be able to prove it to others, such as your manager, a regulator, or an external auditor.
The problem: Shifting the burden of proof
In the history of software and technology, we often operate under the assumption that a product is assumed safe until a failure occurs.
In industries where failure is catastrophic, such as aerospace, nuclear power, or rail systems, this assumption is unacceptable. Aircraft are not deployed to production environments to validate safety through failure. The introduction of powerful, opaque AI systems into critical sectors, such as healthcare, automotive, and finance, requires applying the same pre-deployment safety and verification standards.
This shifts the burden of proof to the teams and organizations that build and deploy the system.
The solution: The AI safety case
The solution comes from established engineering disciplines. Safety-critical industries rely on a formal deliverable called the safety case. We define a safety case as a structured argument, supported by evidence, intended to justify that a system is acceptably safe for a specific application in a specific operating environment.
This is not just a document; it’s a way of thinking. It requires you to systematically:
Identify hazards: What are all the ways this system could fail and cause harm?
Unlike a valve that has two states (Open/Closed), an LLM has an infinite failure modes. Therefore, hazard analysis for AI cannot be exhaustive; it must be scenario-based (focusing on the most likely and most severe impacts) rather than trying to enumerate every possible text output.
Define control: What safety mechanisms are in place?
Provide evidence: Prove, using hard data (your PGD results, your SHAP reports), that the controls work and the risk is acceptable.
The safety case represents a shift from compliance-based regulation (following a checklist) to goal-based regulation (achieving a safety goal and demonstrating its achievement). Next, we will explore the BIG argument, a modern framework specifically designed to structure the safety case for a complex AI system.
Assuring a sociotechnical system
When assessing the safety of an airplane or a nuclear reactor, the system is largely physical and operates within well-defined constraints. Ensuring the safety of an AI system is fundamentally harder because it is sociotechnical. Risk arises not only from the model’s parameters but from how the system interacts with users, organizational processes, and broader social contexts. To manage this complexity, safety researchers often reference the Balanced, Integrated, and Grounded (BIG) argument framework.
The BIG argument framework
The BIG argument proposes that a comprehensive AI Safety Case must satisfy three core characteristics:
Balanced (the ethics component):
Goal: The argument must explicitly address safety alongside other core ethical principles, such as fairness (mitigating bias) and privacy.
Why it matters: Ethical trade-offs are inevitable (e.g., increasing transparency may reveal trade secrets ). A Balanced argument forces the developer to acknowledge and justify these conflicts to affected stakeholders (like regulators or ...