Abduction-based explanations for Machine Learning models

Alexey Ignatiev, Nina Narodytska, Joao Marques-Silva

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

The growing range of applications of Machine Learning (ML) in a multitude of settings motivates the ability of computing small explanations for predictions made. Small explanations are generally accepted as easier for human decision makers to understand. Most earlier work on computing explanations is based on heuristic approaches, providing no guarantees of quality, in terms of how close such solutions are from cardinality- or subset-minimal explanations. This paper develops a constraint-agnostic solution for computing explanations for any ML model. The proposed solution exploits abductive reasoning, and imposes the requirement that the ML model can be represented as sets of constraints using some target constraint reasoning system for which the decision problem can be answered with some oracle. The experimental results, obtained on well-known datasets, validate the scalability of the proposed approach as well as the quality of the computed solutions.
Original languageEnglish
Title of host publicationProceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence
EditorsPascal Van Hentenryck, Zhi-Hua Zhou
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages1511-1519
Number of pages9
ISBN (Electronic)9781577358091
DOIs
Publication statusPublished - 2019
Externally publishedYes
EventAAAI conference on Artificial Intelligence 2019 - Honolulu, United States of America
Duration: 27 Jan 20191 Feb 2019
Conference number: 33rd
https://aaai.org/Conferences/AAAI-19/

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Number1
Volume33
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceAAAI conference on Artificial Intelligence 2019
Abbreviated titleAAAI 2019
CountryUnited States of America
CityHonolulu
Period27/01/191/02/19
Internet address

Cite this

Ignatiev, A., Narodytska, N., & Marques-Silva, J. (2019). Abduction-based explanations for Machine Learning models. In P. Van Hentenryck, & Z-H. Zhou (Eds.), Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence (pp. 1511-1519). (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 33, No. 1). Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI). https://doi.org/10.1609/aaai.v33i01.33011511