Podcast episode with Cynthia Rudin, Professor of Computer Science and director of the Prediction Analysis Lab, whose main focus is in interpretable machine learning, at Duke University.
Deep neural networks are undeniably effective. They rely on such a high number of parameters, that they 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 achieving “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
Podcast Series: Data Skeptic