Towards trustable explainable AI

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Abstract

Explainable artificial intelligence (XAI) represents arguably one of the most crucial challenges being faced by the area of AI these days. Although the majority of approaches to XAI are of heuristic nature, recent work proposed the use of abductive reasoning to computing provably correct explanations for machine learning (ML) predictions. The proposed rigorous approach was shown to be useful not only for computing trustable explanations but also for validating explanations computed heuristically. It was also applied to uncover a close relationship between XAI and verification of ML models. This paper overviews the advances of the rigorous logic-based approach to XAI and argues that it is indispensable if trustable XAI is of concern
Original languageEnglish
Title of host publicationProceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
EditorsChristian Bessiere
Place of PublicationMarina del Rey CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages5154-5158
Number of pages5
ISBN (Electronic)9780999241165
DOIs
Publication statusPublished - 2020
EventInternational Joint Conference on Artificial Intelligence-Pacific Rim International Conference on Artificial Intelligence 2020 - Yokohama, Japan
Duration: 7 Jan 202115 Jan 2021
Conference number: 29th/17th
https://www.ijcai.org/Proceedings/2020/ (Proceedings)
https://ijcai20.org (Website)

Conference

ConferenceInternational Joint Conference on Artificial Intelligence-Pacific Rim International Conference on Artificial Intelligence 2020
Abbreviated titleIJCAI-PRICAI 2020
CountryJapan
CityYokohama
Period7/01/2115/01/21
OtherIJCAI-PRICAI 2020, the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence!IJCAI-PRICAI2020 will take place January 7-15, 2021 online in a virtual reality in Japanese Standard Time (JST) zone.
Internet address

Keywords

  • Machine Learning
  • Explainable Machine Learning
  • Classification
  • Constraints and SAT
  • Constraints and Data Mining
  • Constraints and Machine Learning
  • Multidisciplinary Topics and Applications
  • Validation and Verification

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