# The BIG Argument for AI Safety Cases

arXiv: https://arxiv.org/abs/2503.11705 · HTML: https://mlsanity.com/paper/2503.11705/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2025.

## Built on by (most cited first, 12 of 12)

- [Metrics that matter: Evaluating image quality metrics for medical image generation](https://mlsanity.com/paper/2505.07175/) (2025)
- [Justified Evidence Collection for Argument-based AI Fairness Assurance](https://mlsanity.com/paper/2505.08064/) (2025)
- [Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systems](https://mlsanity.com/paper/2510.21254/) (2025)
- [A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace](https://mlsanity.com/paper/2601.03120/) (2026)
- [EvoXplain: When Machine Learning Models Agree on Predictions but Disagree on Why -- Measuring Mechanistic Multiplicity Across Training Runs](https://mlsanity.com/paper/2512.22240/) (2025)
- [Clear, Compelling Arguments: Rethinking the Foundations of Frontier AI Safety Cases](https://mlsanity.com/paper/2603.08760/) (2026)
- [What Types of Human-AI Teams Exist?](https://mlsanity.com/paper/2607.02198/) (2026)
- [Safety Case Patterns for VLA-based driving systems: Insights from SimLingo](https://mlsanity.com/paper/2603.16013/) (2026)
- [AI Assurance in UK Defence: Challenges in Operationalising JSP 936](https://mlsanity.com/paper/2606.09414/) (2026)
- [I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contexts](https://mlsanity.com/paper/2606.14327/) (2026)
- [The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems](https://mlsanity.com/paper/2607.19292/) (2026)
- [The Wisdom of Artificial Deliberative Crowds](https://mlsanity.com/paper/2609.22497/) (2026)

## Abstract (arXiv)

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. Firstly, it is balanced by addressing safety alongside other critical ethical issues such as privacy and equity, acknowledging complexities and trade-offs in the broader societal impact of AI. Secondly, it is integrated by bringing together the social, ethical and technical aspects of safety assurance in a way that is traceable and accountable. Thirdly, it is grounded in long-established safety norms and practices, such as being sensitive to context and maintaining risk proportionality. 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 systematic treatment of safety. 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 wider AI safety case, approaching assurance from both technical and sociotechnical perspectives. Examples illustrating the use of the BIG argument are provided throughout the paper.