30th International Conference on Case-Based Reasoning September 2022, in Nancy, France



Session 1 (09:00 - 10:30) Chair: Juan Antonio Recio Garcia
Counterfactuals as Explanations for Monotonic Classifiers by Sarathi K, Shania Mitra, Deepak P and Sutanu Chakraborti
When a CBR in Hand is Better than Twins in the Bush by Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Mir Riyanul Islam and Rosina Weber
Applying explanation methods for the iterative refinement of an ANN-based depression screening tool by Cristian E. Sosa-Espadas, Manuel E. Cetina-Aguilar, Jose A. Soladrero, Jesus M. Darias, Esteban E. Brito-Borges, Nora Cuevas-Cuevas and Mauricio Gabriel Orozco-del-Castillo
Case-Based Explanation: Making the Implicit Explicit by David Leake
Coffee Break (10:30-11:00)
Session 2 (11:00 - 13:00) Chair: Maria Belen Diaz Agudo
Conceptual Modelling of Explanation Experiences Through the iSeeOnto Ontology by Marta Caro-Martínez, Anjana Wijekoon, Juan A. Recio-García, David Corsar and Ikechukwu Nkisi-Orji
A Case-based Explanation Method for Weather Forecasting by Moisés Fernando Valdez-Ávila, Gerardo Arturo Pérez-Pérez, Humberto Sarabia-Osorio, Carlos Bermejo-Sabbagh and Mauricio Gabriel Orozco-Del-Castillo
Activity Recognition and Explanations for Cancer Health Awareness by Hayley Borck, Jack Ladwig, Joseph Mueller, Steven Johnston, Helen Wauck, Ruta Wheelock and Rick Freedman
Generating Counterfactual Images: Towards a C2C-VAE Approach by Ziwei Zhao, David Leake, Xiaomeng Ye and David Crandall
Unsupervised clustering applied to the optimization of a Case-based Reasoning system for the selection of optimal image explanation methods by Esteban E. Brito-Borges, Mauricio G. Orozco-del-Castillo and Juan A. Recio-Garcia
Lunch Break (13:00-14:30)
Session 3 (14:30 - 16:30) XCBR Challenge, Chair: Kyle Martin
Coffee Break (16:30-17:00)
Session 4 (17:00 - 18:00) XCBR-Challenge Prizes, Chair: Anne Liret

Discussion and Closing Remarks


XCBR is a workshop aiming to provide a medium of exchange for information about trends, research issues and practical experiences in the use of Case-based Reasoning (CBR) methods for the inclusion of explanations to several AI techniques (including CBR itself).

The success of the intelligent systems has led to an explosion of the generation of new autonomous systems with new capabilities like perception, reasoning, decision support and self-actioning. Despite the tremendous benefits of these systems, they work as black-box systems and their effectiveness is limited by their inability to explain their decisions and actions to human users. The problem of explainability in Artificial Intelligence is not new but the rise of 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 in order to increase users’ reliability in these systems. Additionally, the European Union included in their regulation about the protection of natural persons with regard to the processing of personal data a new directive about the need for explanations to ensure fair and transparent processing in automated decision-making 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 is intended to have a structure of activities that helps the 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. CBR systems have previous experiences in interactive explanations and in exploiting memory-based techniques to generate these explanations that can be successfully applied to the explanation of emerging AI and machine learning techniques.

Research contributions submitted to the workshop will be related to areas that include, but are not limited to, the following:

  • AI explanation methods using CBR: CBR explanations of ML techniques, planning, recommender systems, decision-making techniques
  • CBR for explaining data obtained from sensor systems, Internet of Things, or wearables
  • Explanations of complex CBR systems
  • Hybrid CBR models to provide explanation capabilities
  • Case-based explanation capabilities for different domains
  • Lessons learned in XCBR investigations
  • Challenge tasks for XCBR systems in novel AI techniques
  • Measures and methods of evaluation for assessing case-based explanations
  • User interaction for explanations
  • The role of experience on explainability.


We invite submissions of two types:

  • Long research and application papers: a maximum of 12 pages describing original contributions.
  • Short position papers: a maximum of 6 pages describing new research ideas and partially developed frameworks

Papers must be submitted in electronic form as a PDF. CEUR template is the format required for the final camera-ready copy. Authors’ instructions along with LaTeX and Word macro files are available on the web at https://de.overleaf.com/2519757288ymcbnsvknvqb .

Please submit your work via the EasyChair system. Please use EasyChair at https://easychair.org/conferences/?conf=iccbr2022. Make sure to select the track “Workshop on CBR for the Explanation of Intelligent Systems..

XAI Challenge 2022

XCBR 2022 is also organising a contest on explicability using CBR. This challenge will consist of several tasks related to the generation of explanation experiences and the application of case-based explanation solutions to machine learning black-boxes. Read more here…


  • Paper submission deadline: July 8, 2022
  • XAI Challenge submission deadline: August 26, 2022
  • Workshop paper Notification of acceptance: July 22, 2022
  • Workshop paper Camera-ready submission: July 29, 2022
  • Workshop date: September 12, 2022


  • David Aha, Naval Research Laboratory, USA
  • Derek Bridge, University College Cork, Ireland
  • Mark Keane, University College Dublin, Ireland
  • David Leake, Indiana University, USA
  • Rosina Weber, Dexel University, USA
  • Enric Plaza, IIIA, CSIC, Spain
  • Jakob Schönborn, Universität Hildesheim, Germany
  • Kyle Martin, Robert Gordon University, UK
  • Nirmalie Wiratunga, Robert Gordon University, UK
  • Bruno Fleisch, BT France
  • Anne Liret, BT France
  • David Corsar, Robert Gordon University, UK
  • Ike Nkisi-Orji, Robert Gordon University, UK


  • Juan A. Recio García, University Complutense of Madrid, Spain
  • Belén Díaz Agudo, University Complutense of Madrid, Spain
  • Anjana Wijekoon, Robert Gordon University, Scotland