Sixth Workshop on XCBR: Case-Based Reasoning for the Explanation of Intelligent Systems

July 1 Merida, Yucatan, Mexico

XCBR workshop aims to provide a medium of exchange for information about trends, research issues and practical experiences in the use of Case-Based Reasoning (CBR) for the inclusion of explanations to several AI techniques (including CBR itself). CBR provides opportunities for exploiting memory-based techniques to generate these explanations that can be successfully applied to the explanation of emerging AI and machine learning techniques. The problem of explainability in Artificial Intelligence is not new but the rise of the autonomous intelligent systems has created the necessity to understand how these intelligent systems achieve a solution, make a prediction or a recommendation or reason to support a decision to increase users’ trust in these systems.
The goal of Explainable Artificial Intelligence (XAI) is “to create a suite of new or modified machine learning techniques that produce explainable models that, when combined with effective explanation techniques, enable end users to understand, appropriately trust, and effectively manage the emerging generation of Artificial Intelligence (AI) systems”.
For this purpose, the XCBR workshop helps an exchange of ideas and interaction, suited to highlight the main bottlenecks and challenges, as well as the more promising research lines, for CBR research related to the explanation of intelligent systems.

Topics of Interest

  • AI explanation methods using CBR: CBR explanations of ML techniques, planning, recommender systems, decision-making techniques.
  • Explanations of complex CBR systems.
  • Hybrid CBR models to provide explanation capabilities.
  • Generative AI and XCBR.
  • Evaluation metrics, methods and measures for XAI and XCBR.
  • Case-based explanation capabilities for different domains.
  • Ethics and legal regulation of AI (e.g. carrying out the new AI law in the EU). - Interfaces to show case-based explanations.
  • Lessons learned in XCBR investigations.
  • Challenge tasks for XCBR systems in novel AI techniques.
  • User interaction for explanations.
  • The role of experience on explainability.


This workshop will be held on July 1st, 2024 as part of the ICCBR 2024 workshop series in Merida, Mexico. This workshop is open to all interested conference participants. The Organizing Committee will select a subset of the submitted papers for oral presentation.

Accepted papers will be included in the CEUR-WS proceedings, indexed by SCOPUS and Google Scholar, ensuring wide dissemination of your valuable work.

For a paper to appear in the proceedings, at least one of the authors must register for the AI track by the camera-ready copy deadline. Papers must be presented by one of the authors at the conference live. There will be no video presentations.


Please, check the workshop website for submission instructions.

Important Dates

Submission Deadline: April 1, 2024
Notification of Acceptance: April 17, 2024
Camera-ready Version: May 1, 2024
Special Track Date: July 1, 2024


Workshop Chairs

Marta Caro-Martinez, Complutense University of Madrid, Spain
Belén Díaz-Agudo, Complutense University of Madrid, Spain

Program Committee (Tentative)

Belén Díaz Agudo, Universidad Complutense de Madrid, Spain
Marta Caro Martinez, Universidad Complutense de Madrid, Spain
Juan A. Recio García, Universidad Complutense de Madrid, Spain
Nirmalie Wiratunga, Robert Gordon University, UK
Derek Bridge, University College Cork, Ireland
Rosina Weber, Drexel University, USA
Mark Keane, University College Dublin, Ireland
David Leake, Indiana University, USA
Anjana Wijekoon, Robert Gordon University, UK
Kyle Martin, Robert Gordon University, UK
Ikechukwu Nkisi-Orji, Robert Gordon University, UK
Anne Liret, BT France, France
Bruno Freisch, BT France, France

Contact Information

For further details, visit the website at, or contact the special track chairs.