Machine learning interpretability, explainability and trustability

Current progress and next steps

Learn more Register now Add to Calendar 03/11/2020 06:00 PM 03/11/2020 08:30 PM Europe/London Machine learning interpretability, explainability and trustability Location of the event
Wednesday 11 Mar 2020
Time: 18:00 - 20:30

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The ability to interpret the predictions of a machine learning model brings about user trust and supports understanding of the underlying processes being modeled. In many application domains, such as the medical, insurance and criminal justice domains, model interpretability and explainability can be a crucial requirement for the deployment of machine learning, since a model’s predictions would inform critical decision-making. Unfortunately, most state-of-the-art models — such as ensemble models, kernel methods, and neural networks — are perceived as being complex “black-boxes”, the predictions of which are too hard to be interpreted.

About the event

In this seminar, we will outline the challenges in achieving machine learning model interpretability, explainability and trustability. We will then present research progress in turning “black-box” models into “white-box” models. We also introduce key ideas on how to develop more interpretable algorithms for risk prediction, time-series prediction and treatment effects as well as how to test and communicate the goal of interpretability, explainability and trustability is achieved. We will conclude by defining the research agenda that lies ahead.


18:00 - 18:30 - Registration, tea and coffee

18:30 - 18:35 - Introduction

18:35 - 19:25 - Machine learning interpretability, explainability and trustability

19:25 - 19:40 - Q&A

19:40 - 20:30 - Networking reception


Professor Mihaela van der Schaar

Turing Fellow, John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and Founder and Director of the Cambridge Centre for AI in Medicine




1 Wimpole St

London, W1G 0LZ

51.516132120067, -0.14696865692008