Constructing Safety Cases for AI Systems: A Reusable Template Framework

• URL: http://arxiv.org/abs/2601.22773v1
• Date: Fri, 30 Jan 2026 09:53:22 GMT
• Title: Constructing Safety Cases for AI Systems: A Reusable Template Framework
• Authors: Sung Une Lee, Liming Zhu, Md Shamsujjoha, Liming Dong, Qinghua Lu, Jieshan Chen,
• Abstract summary: Safety cases, structured arguments that a system is acceptably safe, are becoming central to the governance of AI systems.<n>Traditional safety-case practices from aviation or nuclear engineering rely on well-specified system boundaries, stable architectures, and known failure modes.<n>This study examines how safety cases are currently constructed for AI systems and why classical approaches fail to capture these dynamics.
• Score: 10.44708664414503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Safety cases, structured arguments that a system is acceptably safe, are becoming central to the governance of AI systems. Yet, traditional safety-case practices from aviation or nuclear engineering rely on well-specified system boundaries, stable architectures, and known failure modes. Modern AI systems such as generative and agentic AI are the opposite. Their capabilities emerge unpredictably from low-level training objectives, their behaviour varies with prompts, and their risk profiles shift through fine-tuning, scaffolding, or deployment context. This study examines how safety cases are currently constructed for AI systems and why classical approaches fail to capture these dynamics. It then proposes a framework of reusable safety-case templates, each following a predefined structure of claims, arguments, and evidence tailored for AI systems. The framework introduces comprehensive taxonomies for AI-specific claim types (assertion-based, constrained-based, capability-based), argument types (demonstrative, comparative, causal/explanatory, risk-based, and normative), and evidence families (empirical, mechanistic, comparative, expert-driven, formal methods, operational/field data, and model-based). Each template is illustrated through end-to-end patterns addressing distinctive challenges such as evaluation without ground truth, dynamic model updates, and threshold-based risk decisions. The result is a systematic, composable, and reusable approach to constructing and maintaining safety cases that are credible, auditable, and adaptive to the evolving behaviour of generative and frontier AI systems.

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