Score-Based Explanations in Data Management and Machine Learning: An Answer-Set Programming Approach to Counterfactual Analysis
Artificial Intelligence
2021-09-21 v2 Databases
Machine Learning
Logic in Computer Science
Abstract
We describe some recent approaches to score-based explanations for query answers in databases and outcomes from classification models in machine learning. The focus is on work done by the author and collaborators. Special emphasis is placed on declarative approaches based on answer-set programming to the use of counterfactual reasoning for score specification and computation. Several examples that illustrate the flexibility of these methods are shown.
Cite
@article{arxiv.2106.10562,
title = {Score-Based Explanations in Data Management and Machine Learning: An Answer-Set Programming Approach to Counterfactual Analysis},
author = {Leopoldo Bertossi},
journal= {arXiv preprint arXiv:2106.10562},
year = {2021}
}
Comments
Revised version for camera ready. Typos corrected, new references, and a new section with background material added. Paper associated to forthcoming short course at Fall School. arXiv admin note: text overlap with arXiv:2007.12799