47 citations · 155 across the 18 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…