Explanations in Data Management and Artificial Intelligence (Sept. 22)
[The video] [The slides]
ExplainableAI is one of the most active areas of research in AI and machine learning in these days. In this tutorial we will review some recent approaches to providing explanations in data management, knowledge representation and machine learning. In particular, causality-based approaches, model-based diagnosis, and score-based explanations will be described and applied to query answering and outcomes of classification models. We will also show how answer-set programming can be used for reasoning about counterfactual causes for model results.
Leopoldo Bertossi
Leopoldo Bertossi has been Full Professor at the School of Computer Science, Carleton University (Ottawa, Canada) from 2001 to 2019. In September 2019 he took up a full-professorship at Universidad Adolfo Ibañez (UAI, Chile), the oldest and most prestigious fully-private university in Chile. He worked as Senior Computer Scientist at RelationalAI Inc. (Berkeley, CA, USA) Until 2020. He is since 2019 a senior member of the "Millenium Research Institute for Foundations of Data" (IMFD, Chile), a 10-year initiative funded by the Government of Chile. He obtained a PhD in Mathematics from the Pontifical Catholic University of Chile (PUC) in 1988, with a PhD thesis on mathematical logic (model theory) under the supervision of Prof. Joerg Flum (University of Freiburg, Germany). Prof. Bertossi's research interests include data science, database theory, data management, semantic web, intelligent information systems, data management for business intelligence, knowledge representation, uncertain reasoning, logic programming, computational logic, and statistical relational learning.
