Thu, Nov 17, 2022 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Shi Feng, Univ of Illinois, Chicago
Talk Title: Pragmatic Interpretability
Series: NL Seminar
Abstract: Abstract: REMINDER
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Machine learning models have been quite successful at emulating human intelligence but their potential as intelligence augmentation is less explored. Part of the challenge is our lack of understanding in how these models work, and this is the problem interpretability is trying to tackle. But most existing interpretability work takes models trained under the emulation paradigm and adds humans into the mix post-hoc-the human's role is largely an afterthought. In this talk, I advocate for a more pragmatic approach to interpretability and emphasize modeling the human's needs in their cooperation with AIs. In the first part, I discuss how the human-AI team can be evaluated and optimized as a unified decision-maker, and how the model can learn to explain selectively. In the second part, I discuss how human intuition measured outside of the working with an AI context can be incorporated into models and explanations. I'll conclude with a brief discussion on formulating the model's pragmatic inference about its human teammate.
Biography: Shi Feng is a postdoc at University of Chicago working with Chenhao Tan. He got his PhD from University of Maryland under Jordan Boyd-Graber. He is interested in human-AI cooperation: how machine learning can help humans make better decisions, and how humans can provide supervision more effectively. His past work focuses on natural language processing, and covers topics including interpretability, adversarial attacks, robustness, and human-in-the-loop evaluations.
Host: Jon May and Meryem Mhamdi
More Info: https://nlg.isi.edu/nl-seminar/
WebCast Link: https://www.youtube.com/watch?v=C8jUO4w5xwU
Audiences: Everyone Is Invited
Contact: Pete Zamar