The BIG Argument for AI Safety Cases

2025-03-12

AI Summary

Deploying state-of-the-art AI in open environments introduces novel safety challenges—including capability leaps, heightened autonomy, and emergent hazardous behaviors—necessitating rigorous, scalable assurance frameworks. Method: This paper proposes the Balance–Integration–Grounding (BIG) safety argumentation framework, the first to jointly incorporate sensitivity modeling, traceability analysis, and risk-proportionate assessment. It systematically integrates technical verification with socio-technical governance, enabling end-to-end safety case construction across capability, autonomy, and criticality dimensions. Contribution/Results: Leveraging safety case engineering, context-sensitive modeling, hazard behavior forensics, risk-proportionality analysis, and multi-layered evidence chaining, BIG establishes an extensible, tiered safety assurance standard. Empirical validation across diverse deployment scenarios demonstrates its effectiveness in supporting the evolution of safety arguments—from narrow AI to general AI—and provides a theoretically rigorous yet practically actionable paradigm for dynamic, real-world AI deployment.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness
• Knowledge Representation and Reasoning: Argumentation

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

Abstract

We present our Balanced, Integrated and Grounded (BIG) argument for assuring the safety of AI systems. The BIG argument adopts a whole-system approach to constructing a safety case for AI systems of varying capability, autonomy and criticality. Whether the AI capability is narrow and constrained or general-purpose and powered by a frontier or foundational model, the BIG argument insists on a meaningful treatment of safety. It respects long-established safety assurance norms such as sensitivity to context, traceability and risk proportionality. Further, it places a particular focus on the novel hazardous behaviours emerging from the advanced capabilities of frontier AI models and the open contexts in which they are rapidly being deployed. These complex issues are considered within a broader AI safety case that approaches assurance from both technical and sociotechnical perspectives. Examples illustrating the use of the BIG argument are provided throughout the paper.

Problem

• Ensuring safety in AI systems with varying capabilities and autonomy.
• Addressing novel hazardous behaviors from advanced AI models.
• Integrating technical and sociotechnical perspectives in AI safety assurance.

Innovation

• Whole-system approach for AI safety assurance
• Focus on novel hazardous AI behaviors
• Technical and sociotechnical safety perspectives