3 citations · 5 across the 6 of their papers we have counts for
6 papers
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…
Fair by design: A sociotechnical approach to justifying the fairness of AI-enabled systems across the lifecycle
Marten H. L. Kaas, Christopher Burr, Zoe Porter +6
Fairness is one of the most commonly identified ethical principles in existing AI guidelines, and the development of fair AI-enabled systems is required by new and emerging AI regu…
What's my role? Modelling responsibility for AI-based safety-critical systems
Philippa Ryan, Zoe Porter, Joanna Al-Qaddoumi +2
AI-Based Safety-Critical Systems (AI-SCS) are being increasingly deployed in the real world. These can pose a risk of harm to people and the environment. Reducing that risk is an o…
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…
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…