Explaining Black-Box Machine Learning Predictions, Sameer Singh, Ph.D. (UCI) 07-12-2017
Channel: Orange County ACM Chapter
Date: 23/04/2018

Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or "trust" their behavior.  In this talk, Prof. Singh describes approaches to explain the predictions of ANY classifier in an interpretable and faithful manner.  He presents examples of explanations for a variety of data types, such as images, text, and tabular data, and complex classifiers, such as random forests and deep neural networks.  He also presents quantitative evaluation with human subjects on scenarios that require trust: deciding if one should trust a prediction, choosing between two algorithms, improving an untrustworthy algorithm, and predicting the behavior of the classifier.

Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine Donald Bren School of Information and Computer Sciences (ICS).  He is working on large-scale and interpretable machine learning applied to information extraction and natural language processing. Sameer was a Postdoctoral Research Associate at the University of Washington. He received his Ph.D. from the University of Massachusetts, Amherst in 2014, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenges fellowship. Sameer has published at top-tier machine learning and natural language processing conferences and workshops. (http://sameersingh.org) 

This event was co-sponsored by the IEEE Orange County Computer Society.

Video courtesy of UCI  Donald Bren School of Information and Computer Sciences (ICS)