no code implementations • 20 Feb 2023 • Dhaminda B. Abeywickrama, James Wilson, Suet Lee, Greg Chance, Peter D. Winter, Arianna Manzini, Ibrahim Habli, Shane Windsor, Sabine Hauert, Kerstin Eder
The behaviours of a swarm are not explicitly engineered.
no code implementations • 1 Sep 2022 • Shakir Laher, Carla Brackstone, Sara Reis, An Nguyen, Sean White, Ibrahim Habli
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.
no code implementations • 29 Mar 2022 • Zoe Porter, Ibrahim Habli, John McDermid, Marten Kaas
Assurance cases are structured arguments, supported by evidence, that are often used to establish confidence that a software-intensive system, such as an aeroplane, will be acceptably safe in its intended context.
no code implementations • 1 Sep 2021 • Yan Jia, John McDermid, Tom Lawton, Ibrahim Habli
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 which to assess validity.
1 code implementation • 2 Feb 2021 • Richard Hawkins, Colin Paterson, Chiara Picardi, Yan Jia, Radu Calinescu, Ibrahim Habli
Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance.
no code implementations • 11 Jan 2021 • Yan Jia, Tom Lawton, John McDermid, Eric Rojas, Ibrahim Habli
As healthcare is now data rich, it is possible to augment safety analysis with machine learning to discover actual causes of medication error from the data, and to identify where they deviate from what was predicted in the safety analysis.