Interpretability - now what?
berkeley.edu
Interpretability - now what?
In this talk, I hope to reflect on some of the progress made in the field of interpretable machine learning. We will reflect on where we are going as a field, and what are the things we need to be aware and be careful as we make progress. With that perspective, I will then discuss some of my recent work 1) sanity checking popular methods and 2 ...
Jul 17, 2019
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