University of Cambridge > Talks.cam > Machine Learning Reading Group @ CUED > CBL Alumni Talk: Finale Doshi-Velez

CBL Alumni Talk: Finale Doshi-Velez

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If you have a question about this talk, please contact Elre Oldewage.

The volumes of medical data being recorded are now far beyond what human experts can analyze, especially at the bedside. At the same time, a lot of essential information goes unrecorded; any purely machine system will quickly hit fundamental statistical limitations. Thus, to make a positive impact in healthcare, we need robust tools that can communicate their limitations and assumptions to the ultimate human decision-maker.

Whether it is building models, optimizing treatment policies, or validating outputs, my lab addresses this challenge by keeping human-machine validation and integration in mind from the start. In this talk, I will discuss how our pursuit of small, inspectable models lead us to find (and resolve) a decade-old failing of supervised generative models; how those models (seemingly) resulted in better strategies for managing hypotension in the ICU ; how we realized that some of their strategies were (perhaps) bogus and how we regained (some of) our trust via novel ways of incorporating human input into statistical off-policy evaluation; and how our recommendations for interpretability and careful validation are being heard throughout the world. All through the way, I will emphasize the many interesting technical questions whose answers have potential for real impacts in health.

This talk is part of the Machine Learning Reading Group @ CUED series.

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