Understanding the why and how of query results – data provenance – is a well-studied problem in the database community. A variety of approaches have been applied to this problem, ranging from provenance polynomials which explain how an output tuple is constructed from input tuples, to notions of causality and responsibility. More recently, a lot of attention has been paid to explainable AI (XAI). In particular, the use of Shapley values has become increasingly popular as a model-agnostic method for explaining individual predictions.
In this talk, I will discuss connections between provenance and work in XAI. In particular, I will discuss how Shapley values can be used in the context of data provenance and rule-based data insights, and the different perspectives that can be gained. I will also discuss how provenance can be used for incrementally updating machine learning models for linear or logistic regression, as well as in symbolic reasoning for AI applications.
Speakers Bio: Susan B. Davidson received the B.A. degree in Mathematics from Cornell University, Ithaca, NY, in 1978, and the M.A. and Ph.D. degrees in Electrical Engineering and Computer Science from Princeton University, Princeton NJ, in 1980 and 1982. Dr. Davidson is the Weiss Professor of Computer and Information Science (CIS) at the University of Pennsylvania, where she has been since 1982. She was the founding co-director of the Penn Center for Bioinformatics from 1997-2003, the founding co-director of the Greater Philadelphia Bioinformatics Alliance, and served as Deputy Dean of the School of Engineering and Applied Science from 2005-2007 and Chair of CIS from 2008-2013. Her research interests include data management for data science, database and web-based systems, provenance, crowdsourcing, and data citation.
Dr. Davidson is a Fellow of the AAAS, ACM Fellow, Corresponding Fellowship of the Royal Society of Edinburgh, received the Lenore Rowe Williams Award, and was a Fulbright Scholar and recipient of a Hitachi Chair. She received the IEEE Technical Committee of Data Engineering Impact Award, the Lindback Distinguished Teaching Award, the Ruth and Joel Spira Award for Excellence in Teaching, the Trustees' Council of Penn Women/Provost Award for her work on advancing women in engineering, and served as Chair of the board of the Computing Research Association.