47 citations · 155 across the 18 of their papers we have counts for
19 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…
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…
Learning Run-time Safety Monitors for Machine Learning Components
Ozan Vardal, Richard Hawkins, Colin Paterson +4
For machine learning components used as part of autonomous systems (AS) in carrying out critical tasks it is crucial that assurance of the models can be maintained in the face of p…
ACCESS: Assurance Case Centric Engineering of Safety-critical Systems
Ran Wei, Simon Foster, Haitao Mei +7
Assurance cases are used to communicate and assess confidence in critical system properties such as safety and security. Historically, assurance cases have been manually created do…
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…
Ethics in conversation: Building an ethics assurance case for autonomous AI-enabled voice agents in healthcare
Marten H. L. Kaas, Zoe Porter, Ernest Lim +3
The deployment and use of AI systems should be both safe and broadly ethically acceptable. The principles-based ethics assurance argument pattern is one proposal in the AI ethics l…
AERoS: Assurance of Emergent Behaviour in Autonomous Robotic Swarms
Dhaminda B. Abeywickrama, James Wilson, Suet Lee +7
The behaviours of a swarm are not explicitly engineered. Instead, they are an emergent consequence of the interactions of individual agents with each other and their environment. T…
Review of the AMLAS Methodology for Application in Healthcare
Shakir Laher, Carla Brackstone, Sara Reis +3
In recent years, the number of machine learning (ML) technologies gaining regulatory approval for healthcare has increased significantly allowing them to be placed on the market. H…
Guidance on the Safety Assurance of Autonomous Systems in Complex Environments (SACE)
Richard Hawkins, Rob Alexander, Matt Osborne +4
Autonomous systems (AS) are systems that have the capability to take decisions free from direct human control. AS are increasingly being considered for adoption for applications wh…
Analysing Ultra-Wide Band Positioning for Geofencing in a Safety Assurance Context
Victoria Hodge, Richard Hawkins, James Hilder +1
There is a desire to move towards more flexible and automated factories. To enable this, we need to assure the safety of these dynamic factories. This safety assurance must be achi…
A Principles-based Ethics Assurance Argument Pattern for AI and Autonomous Systems
Zoe Porter, Ibrahim Habli, John McDermid +1
An assurance case is a structured argument, typically produced by safety engineers, to communicate confidence that a critical or complex system, such as an aircraft, will be accept…
The Role of Explainability in Assuring Safety of Machine Learning in Healthcare
Yan Jia, John McDermid, Tom Lawton +1
Established approaches to assuring safety-critical systems and software are difficult to apply to systems employing ML where there is no clear, pre-defined specification against wh…
Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)
Richard Hawkins, Colin Paterson, Chiara Picardi +3
Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance. Many of these systems, in domains suc…
A Framework for Assurance of Medication Safety using Machine Learning
Yan Jia, Tom Lawton, John McDermid +2
Medication errors continue to be the leading cause of avoidable patient harm in hospitals. This paper sets out a framework to assure medication safety that combines machine learnin…
Enhancing Covid-19 Decision-Making by Creating an Assurance Case for Simulation Models
Ibrahim Habli, Rob Alexander, Richard Hawkins +4
Simulation models have been informing the COVID-19 policy-making process. These models, therefore, have significant influence on risk of societal harms. But how clearly are the und…
Engineering Trustworthy Self-Adaptive Software with Dynamic Assurance Cases
Radu Calinescu, Danny Weyns, Simos Gerasimou +3
Building on concepts drawn from control theory, self-adaptive software handles environmental and internal uncertainties by dynamically adjusting its architecture and parameters in…
Formalism of Requirements for Safety-Critical Software: Where Does the Benefit Come From?
Ibrahim Habli, Andrew Rae
Safety and assurance standards often rely on the principle that requirements errors can be minimised by expressing the requirements more formally. Although numerous case studies ha…