no code implementations • 13 May 2021 • Iain Barclay, Alun Preece, Ian Taylor, Swapna K. Radha, Jarek Nabrzyski
Adopting shared data resources requires scientists to place trust in the originators of the data.
no code implementations • 5 Mar 2021 • Iain Barclay, Harrison Taylor, Alun Preece, Ian Taylor, Dinesh Verma, Geeth de Mel
Increased adoption of artificial intelligence (AI) systems into scientific workflows will result in an increasing technical debt as the distance between the data scientists and engineers who develop AI system components and scientists, researchers and other users grows.
no code implementations • 8 Jul 2019 • Iain Barclay, Alun Preece, Ian Taylor, Dinesh Verma
Increased adoption and deployment of machine learning (ML) models into business, healthcare and other organisational processes, will result in a growing disconnect between the engineers and researchers who developed the models and the model's users and other stakeholders, such as regulators or auditors.
no code implementations • 25 Sep 2018 • Iain Barclay, Alun Preece, Ian Taylor
Organisations are increasingly open to scrutiny, and need to be able to prove that they operate in a fair and ethical way.