Frontiers in Machine Learning: Saving Lives with Interpretable ML

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Microsoft Research
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Published on ● Video Link: https://www.youtube.com/watch?v=vO7h2-_VwWE


Duration: 1:36:37
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This session is about Saving Lives Using Interpretable Machine Learning in HealthCare. It’s critical to make sure healthcare models are safe to deploy. One challenge is that most patients are receiving treatment and that affects the data. A model might learn high blood pressure is good for you because the treatment given when you have blood pressure lowers risk compared to healthier patients with lower blood pressure. There are many ways confounding can cause models to predict crazy things. In the first presentation Rich Caruana will talk about problems that we see in healthcare data thanks to interpretable machine learning. In the second presentation, Ankur Teredesai from UW will talk about Fairness in Machine Learning for HealthCare. And in the last presentation Marzyeh Ghassemi from Toronto will talk about how Interpretable, Explainable, and Transparent AI can be Dangerous in HealthCare. Looks like an exciting lineup, so please join us!

Session Lead: Rich Caruana, Microsoft

Speaker: Rich Caruana, Microsoft
Talk Title: Saving Lives with Interpretable Machine Learning

Speaker: Ankur Teredesai, University of Washington
Talk Title: Fairness in Healthcare AI

Speaker: Marzyeh Ghassemi, University of Toronto
Talk Title: Expl-AI-n Yourself: The False Hope of Explainable Machine Learning in Healthcare

See more on-demand sessions from Microsoft Research's Frontiers in Machine Learning 2020 virtual event: https://www.microsoft.com/en-us/research/event/frontiers-in-machine-learning-2020/



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Tags:
Frontiers in Machine Learning 2020
Microsoft Research
Interpretable ML
machine learning for healthcare
Rich Caruana
fairness healthcare AI
Ankur Teredesai
Marzyeh Ghassemi