5 papers
I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contexts
Shaun Feakins, Ibrahim Habli, Kim Littler +1
This paper appraises recent frameworks within AI development to integrate LLMs into control tasks in automotive contexts from the perspective of safety assurance. This work has bui…
Clear, Compelling Arguments: Rethinking the Foundations of Frontier AI Safety Cases
Shaun Feakins, Ibrahim Habli, Phillip Morgan
This paper contributes to the nascent debate around safety cases for frontier AI systems. Safety cases are structured, defensible arguments that a system is acceptably safe to depl…
Formal Evidence Generation for Assurance Cases for Robotic Software Models
Fang Yan, Simon Foster, Ana Cavalcanti +2
Robotics and Autonomous Systems are increasingly deployed in safety-critical domains, so that demonstrating their safety is essential. Assurance Cases (ACs) provide structured argu…
Unravelling Responsibility for AI
Zoe Porter, Philippa Ryan, Phillip Morgan +5
It is widely acknowledged that we need to establish where responsibility lies for the outputs and impacts of AI-enabled systems. This is important to achieve justice and compensati…
Upstream and Downstream AI Safety: Both on the Same River?
John McDermid, Yan Jia, Ibrahim Habli
Traditional safety engineering assesses systems in their context of use, e.g. the operational design domain (road layout, speed limits, weather, etc.) for self-driving vehicles (in…