Achieving Understandability and Privacy With Provenance-Based Explanations

Explore a 27-minute lecture on provenance-based explanations for query results, delivered by Amir Gilad from the Hebrew University of Jerusalem at the Simons Institute. Delve into the challenges of applying these tools in complex settings, focusing on the issues of understandability and privacy. Discover two innovative approaches that address these challenges through provenance manipulation. Learn about a method for generating natural language explanations based on provenance, making complex query results more accessible to users. Examine a recent development in ensuring privacy for proprietary queries while still providing provenance-based explanations, using a k-anonymity inspired model. Gain insights into the intersection of data validation, result justification, and the protection of confidential information in query evaluation.