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Machine learning interpretability, explainability and trustability: Defining the research agenda and current progress

Mihaela van der Schaar ( University of Cambridge )

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.
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.

Speaker bio

Professor van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Turing Faculty Fellow at The Alan Turing Institute in London, where she leads the effort on data science and machine learning for personalized medicine. Prior to this, she was a Chancellor's Professor at UCLA and MAN Professor of Quantitative Finance at University of Oxford. She is an IEEE Fellow (2009). She has received the Oon Prize on Preventative Medicine from the University of Cambridge (2018). She has also been the recipient of an NSF Career Award, 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. She holds 35 granted USA patents. Recently NESTA has identified her as the female researcher based in the UK with the most publications in AI.



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