AI for Social Impact Seminar Series - Finale Doshi-Velez


Monday, November 16, 2020, 1:00pm

Join us from August 24 through December 14 at the AI for Social Impact Seminar Series. This seminar series will explore how artificial intelligence can contribute to solving social problems.

Artificial intelligence is poised to play an increasingly large role in societies across the world. Accordingly, there is a growing interest in ensuring that AI is used in a responsible and beneficial manner. A range of perspectives and contributions are needed, spanning the full spectrum from fundamental research to sustained deployments.

This seminar series will explore how artificial intelligence can contribute to solving social problems. For example, what role can AI play in promoting health, access to opportunity, and sustainable development? How can AI initiatives be deployed in an ethical, inclusive, and accountable manner?

Finale Doshi-Velez (Harvard School of Engineering and Applied Sciences)


Antidepressant Recommendation Systems: An Ongoing Journey for Technical and Clinical Impact


Abstract: How to choose antidepressants is an important question for patients who need medications to help manage their depression.  In this talk, I will discuss how our ongoing effort toward clinical relevance -- that is, our search for ways to assist with antidepressant treatment recommendations -- has led to insights in core machine learning (we corrected a decade-old flaw in supervised generative modeling) as well as HCI (how psychiatrists react to incorrect recommendations).  I will share how we select directions with interesting technical questions and societal impact, as well as what kinds of directions are good candidates for ML at all.  (I hope to make some of this a more informal discussion.)

Work in collaboration with Roy Perlis, Tom McCoy, Michael Hughes, Eric Sudderth, Gabe Hope, Leah Weiner, Melanie Pradier, Maia Jacobs, Krzyzstof Gajos. 


Finale Doshi-VelezFinale Doshi-Velez is a John L. Loeb associate professor in Computer Science at the Harvard Paulson School of Engineering and Applied Sciences.  She completed her MSc from the University of Cambridge as a Marshall Scholar, her PhD from MIT, and her postdoc at Harvard Medical School.  Her interests lie at the intersection of machine learning, healthcare, and interpretablity.