Delivering inflated explanations

Yacine Izza, Alexey Ignatiev, Peter J. Stuckey, Joao Marques-Silva

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

Abstract

In the quest for Explainable Artificial Intelligence (XAI) one of the questions that frequently arises given a decision made by an AI system is, “why was the decision made in this way?” Formal approaches to explainability build a formal model of the AI system and use this to reason about the properties of the system. Given a set of feature values for an instance to be explained, and a resulting decision, a formal abductive explanation is a set of features, such that if they take the given values, will always lead to the same decision. This explanation is useful, it shows that only some features were used in making the final decision. But it is narrow, it only shows that if the selected features take their given values the decision is unchanged. It is possible that some features may change values and still lead to the same decision. In this paper we formally define inflated explanations, which is a set of features and for each feature a set of values (always including the value of the instance being explained), such that the decision will remain unchanged, for any of the values allowed for any of the features in the (inflated) abductive explanation. Inflated formal explanations are more informative than common abductive explanations since e.g. they allow us to see if the exact value of a feature is important, or it could be any nearby value. Overall they allow us to better understand the role of each feature in the decision. We show that we can compute inflated explanations for not that much greater cost than abductive explanations, and that we can extend duality results for abductive explanations also to inflated explanations.

Original languageEnglish
Title of host publicationThirty-Eighth AAAI Conference on Artificial Intelligence
EditorsMichael Wooldridge, Jennifer Dy, Sriraam Natarajan
Place of PublicationWashington DC USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages12744-12753
Number of pages10
ISBN (Electronic)9781577358879
DOIs
Publication statusPublished - 2024
EventAAAI Conference on Artificial Intelligence 2024 - Vancouver, Canada
Duration: 20 Feb 202427 Feb 2024
Conference number: 38th
https://ojs.aaai.org/index.php/AAAI/issue/view/588 (AAAI-24 Technical Tracks 13)
https://ojs.aaai.org/index.php/AAAI/issue/view/589 (AAAI-24 Technical Tracks 14)
https://ojs.aaai.org/index.php/AAAI/issue/view/593 (AAAI-24 Technical Tracks 18)
https://aaai.org/aaai-conference/ (Website)

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number11
Volume38
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceAAAI Conference on Artificial Intelligence 2024
Abbreviated titleAAAI 2024
Country/TerritoryCanada
CityVancouver
Period20/02/2427/02/24
Internet address

Keywords

  • ML
  • Transparent
  • Interpretable
  • Explainable ML
  • CSO
  • Satisfiability
  • Constraint Satisfaction
  • KRR
  • Diagnosis and Abductive Reasoning
  • Automated Reasoning and Theorem Proving

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