iSee: A case-based reasoning platform for the design of explanation experiences
Loading...
Date
2024-08-08
Authors
Caro-Martínez
Recio-García, Juan A.
Díaz-Agudo, Belén
Darias, Jesus M.
Wiratunga, Nirmalie
Martin, Kyle
Wijekoon, Anjana
Nkisi-Orji, Ikechukwu
Corsar, David
Pradeep, Preeja
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Published Version
Abstract
Explainable Artificial Intelligence (XAI) is an emerging field within Artificial Intelligence (AI) that has provided many methods that enable humans to understand and interpret the outcomes of AI systems. However, deciding on the best explanation approach for a given AI problem is currently a challenging decision-making task. This paper presents the iSee project, which aims to address some of the XAI challenges by providing a unifying platform where personalized explanation experiences are generated using Case-Based Reasoning. An explanation experience includes the proposed solution to a particular explainability problem and its corresponding evaluation, provided by the end user. The ultimate goal is to provide an open catalog of explanation experiences that can be transferred to other scenarios where trustworthy AI is required.
Description
Keywords
eXplainable artificial intelligence , Trustworthy AI , Case-based reasoning , Artificial Intelligence (AI)
Citation
Caro-Martínez, M., Recio-García, J.A., Díaz-Agudo, B., Darias, J.M., Wiratunga, N., Martin, K., Wijekoon, A., Nkisi-Orji, I., Corsar, D., Pradeep, P., Bridge, D. and Liret, A. (2024) ‘Isee: a case-based reasoning platform for the design of explanation experiences’, Knowledge-Based Systems, 302, 112305 (19 pp). Available at: https://doi.org/10.1016/j.knosys.2024.112305