Connectionists: ICBINB Monthly Seminar Series Talk: Finale Doshi-Velez

[Francisco J. Rodríguez Ruiz]

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.*

*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
<https://twitter.com/ICBINBWorkshop>, or email us at
cant.believe.it.is.not.better at gmail.com.

--
Best wishes,
The ICBINB Organizers
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