TY - GEN
T1 - CBR Driven Interactive Explainable AI
AU - Wijekoon, Anjana
AU - Wiratunga, Nirmalie
AU - Martin, Kyle
AU - Corsar, David
AU - Nkisi-Orji, Ikechukwu
AU - Palihawadana, Chamath
AU - Bridge, Derek
AU - Pradeep, Preeja
AU - Agudo, Belen Diaz
AU - Caro-Martínez, Marta
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Explainable AI (XAI) can greatly enhance user trust and satisfaction in AI-assisted decision-making processes. Numerous explanation techniques (explainers) exist in the literature, and recent findings suggest that addressing multiple user needs requires employing a combination of these explainers. We refer to such combinations as explanation strategies. This paper introduces iSee - Intelligent Sharing of Explanation Experience, an interactive platform that facilitates the reuse of explanation strategies and promotes best practices in XAI by employing the Case-based Reasoning (CBR) paradigm. iSee uses an ontology-guided approach to effectively capture explanation requirements, while a behaviour tree-driven conversational chatbot captures user experiences of interacting with the explanations and provides feedback. In a case study, we illustrate the iSee CBR system capabilities by formalising a real-world radiograph fracture detection system and demonstrating how each interactive tools facilitate the CBR processes.
AB - Explainable AI (XAI) can greatly enhance user trust and satisfaction in AI-assisted decision-making processes. Numerous explanation techniques (explainers) exist in the literature, and recent findings suggest that addressing multiple user needs requires employing a combination of these explainers. We refer to such combinations as explanation strategies. This paper introduces iSee - Intelligent Sharing of Explanation Experience, an interactive platform that facilitates the reuse of explanation strategies and promotes best practices in XAI by employing the Case-based Reasoning (CBR) paradigm. iSee uses an ontology-guided approach to effectively capture explanation requirements, while a behaviour tree-driven conversational chatbot captures user experiences of interacting with the explanations and provides feedback. In a case study, we illustrate the iSee CBR system capabilities by formalising a real-world radiograph fracture detection system and demonstrating how each interactive tools facilitate the CBR processes.
KW - Conversational AI
KW - Interactive XAI
KW - Ontology-based CBR
UR - https://www.scopus.com/pages/publications/85172258123
U2 - 10.1007/978-3-031-40177-0_11
DO - 10.1007/978-3-031-40177-0_11
M3 - Conference proceeding
AN - SCOPUS:85172258123
SN - 9783031401763
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 169
EP - 184
BT - Case-Based Reasoning Research and Development - 31st International Conference, ICCBR 2023, Proceedings
A2 - Massie, Stewart
A2 - Chakraborti, Sutanu
PB - Springer Science and Business Media Deutschland GmbH
T2 - Case-Based Reasoning Research and Development - 31st International Conference, ICCBR 2023, Proceedings
Y2 - 17 July 2023 through 20 July 2023
ER -