Wednesday 1 May 2019

07:45

Explainable AI: what do we want and how can we get it?

Adrian Weller & Zoë Webster in conversation with Tabitha Goldstaub

Explainable AI (XAI) is often touted as the solution to our uneasiness about AI. Unlike ‘black box’ machine learning (where even the designers cannot explain why decisions have been reached) its actions can be easily understood – and therefore trusted – by humans. 

What should we really be striving for in this area and how likely are we to be able to achieve it? Is there an inevitable trade-off between the power of AI and its transparency? Hear what our panel of experts have to say on these critical questions in the debate around AI and society.

Adrian Weller

Adrian is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, where he is also a Turing Fellow leading a group on Fairness, Transparency and Privacy.

He is a Senior Research Fellow in Machine Learning at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he leads the project on Trust and Transparency. He serves on several boards including the Centre for Data Ethics and Innovation. Previously, Adrian held senior roles in finance.

This event is part of our ongoing partnership with QuantumBlack around artificial intelligence.

Adrian Weller

Adrian is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, where he is also a Turing Fellow leading a group on Fairness, Transparency and Privacy.

He is a Senior Research Fellow in Machine Learning at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he leads the project on Trust and Transparency. He serves on several boards including the Centre for Data Ethics and Innovation. Previously, Adrian held senior roles in finance.

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