PODS 2024: Keynote Talk
A Data Management Approach to Explainable AI
Speaker: Marcelo Arenas (UC Chile)
Abstract
In recent years, there has been a growing interest in developing methods to explain individual predictions made by machine learning models. This has led to the development of various notions of explanation and scores to justify a model's classification. However, instead of struggling with the increasing number of such notions, one can turn to an old tradition in databases and develop a declarative query language for interpretability tasks, which would allow users to specify and test their own explainability queries. Not surprisingly, logic is a suitable declarative language for this task, as it has a well-understood syntax and semantics, and there are many tools available to study its expressiveness and the complexity of the query evaluation problem. In this talk, we will discuss some recent work on developing such a logic for model interpretability.
Short Bio
Marcelo Arenas is a Professor at the Department of Computer Science and the Institute for Mathematical and Computational Engineering, at the Pontificia Universidad Católica de Chile. He is a Distinguished Member of the Association for Computing Machinery (ACM), the former director of the Millennium Institute for Foundational Research on Data and of the Center for Semantic Web Research. He received a Ph.D. from the University of Toronto in 2005. His research interests are in the areas of data management and applications of logic in computer science. He has received a SIGMOD Jim Gray Doctoral Dissertation Award Honorable Mention in 2006 for his Ph.D. dissertation "Design Principles for XML Data", the 2016 Semantic Web Science Association (SWSA) Ten-Year Award for the article "Semantics and Complexity of SPARQL" and nine best paper awards. He has served on multiple program committees and editorial boards, and has chaired the program committees of ICDT 2015, ISWC 2015, and PODS 2018.
