black-boxes-are-not-required | episodes


listen on castbox.fmlisten on google podcastslisten on player.fmlisten on pocketcastslisten on podcast addictlisten on tuninlisten on Amazon Musiclisten on Stitcher

--:--
--:--


Black Boxes are not Required

Deep neural networks are undeniably effective. They rely on such a high number of parameters, that htey are appropriately described as “black boxes”.

While black boxes lack desirably properties like interpretability and explainability, in some cases, their accuracy makes them incredibly useful.

But does achiving “usefulness” require a black box? Can we be sure an equally valid but simpler solution does not exist?

Cynthia Rudin helps us answer that question. We discuss her recent paper with co-author Joanna Radin Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From An Explainable AI Competition.

Cynthia Rudin

Cynthia Rudin is a professor of computer science, electrical and computer engineering, and statistical science at Duke University. Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. Her degrees are from the University at Buffalo and Princeton University. She is a three-time winner of the INFORMS Innovative Applications in Analytics Award. She has served on committees for INFORMS, the National Academies, the American Statistical Association, DARPA, the NIJ, and AAAI. She is a fellow of both the American Statistical Association and Institute of Mathematical Statistics. She is a Thomas Langford Lecturer at Duke University for 2019-2020.