ICBINB Monthly Seminar Series Talk: Finale Doshi-Velez
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Francisco J. R. Ruiz
Jun 2, 2022, 9:11:32 PM6/2/22
to Machine Learning News
Dear all,
We are pleased to announce that the next speaker of the “I Can’t Believe It’s Not Better!” (ICBINB) virtual seminar series will be Finale Doshi-Velez (Harvard University). More details about this series and the talk are below.
The "I Can't Believe It's Not Better!" (ICBINB) monthly online seminar series seeks to shine a light on the "stuck" phase of research. Speakers will tell us about their most beautiful ideas that didn't "work", about when theory didn't match practice, or perhaps just when the going got tough. These talks will let us peek inside the file drawer of unexpected results and peer behind the curtain to see the real story of how real researchers did real research.
When: June 16th, 2022 at 10am EDT / 4pm CEST
(Note: This talk is happening on the 3rd Thursday of June.)
Where: RSVP for the Zoom link here: https://us02web.zoom.us/meeting/register/tZAtf-morzMsGdSvoCNiIp9OECQkyVBjgygn
Title: Research Process for Interpretable Machine Learning
Abstract: There has been much interest in interpretable machine learning (and/or explainable AI) as a way to allow domain experts to vet machine learning systems as well as a way to assist in human+AI teaming. In this "chalk" talk, I'll briefly provide a framework for thinking about the interdisciplinary ecosystem that interpretable machine learning provides and then dive into the process of doing high-quality, impactful machine learning research. Specifically, I'll talk about:
- What are the kinds of interpretable machine learning questions that are computational and what are human factors?
- How and when should we define abstractions between computational and human factor elements in interpretable machine learning?
- When is a user study needed, and how should it be set up?
In the spirit of ICBINB, I'll draw my own experience, including examples of times when I think we got things right, and when we could have done better.
Abstract: There has been much interest in interpretable machine learning (and/or explainable AI) as a way to allow domain experts to vet machine learning systems as well as a way to assist in human+AI teaming. In this "chalk" talk, I'll briefly provide a framework for thinking about the interdisciplinary ecosystem that interpretable machine learning provides and then dive into the process of doing high-quality, impactful machine learning research. Specifically, I'll talk about:
- What are the kinds of interpretable machine learning questions that are computational and what are human factors?
- How and when should we define abstractions between computational and human factor elements in interpretable machine learning?
- When is a user study needed, and how should it be set up?
In the spirit of ICBINB, I'll draw my own experience, including examples of times when I think we got things right, and when we could have done better.
Bio: Finale Doshi-Velez is a Gordon McKay 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 interpretability.
For more information and for ways to get involved, please visit us at http://icbinb.cc/, Tweet to us @ICBINBWorkhop, or email us at cant.believe.i...@gmail.com.
Best wishes,
The ICBINB OrganizersReply all
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