The BIG Argument for AI Safety Cases

Safety requires balancing multiple ethical concerns, not just technical fixes. The BIG argument emphasizes that AI safety assurance must address privacy and equity alongside safety itself, acknowledging that trade-offs exist between these competing values rather than treating them as separate problems.

Technical and social perspectives must work together. The paper argues for an "integrated" approach that brings together technical safety measures with social and ethical considerations in a traceable, accountable way—meaning engineers, ethicists, and policymakers need to collaborate rather than work in silos.

Different AI systems need proportional safety approaches. Rather than applying one-size-fits-all rules, the BIG argument grounds safety practices in context-sensitivity and risk proportionality, meaning a narrow AI tool used in low-stakes settings requires different assurance than a powerful general-purpose model deployed widely.

Frontier AI models pose novel hazards that demand systematic attention. The paper highlights that advanced AI systems are exhibiting new dangerous behaviors that weren't present in earlier systems, and these emerging risks require deliberate, systematic safety cases rather than assuming existing practices are sufficient.

Safety cases should be comprehensive and documented. The BIG argument calls for whole-system safety documentation that is grounded in established safety practices from other industries, making it possible to trace decisions and hold organizations accountable for their safety reasoning.