iSee: intelligent sharing of explanation experiences
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Published Version
Date
2022
Authors
Martin, Kyle
Wijekoon, Anjana
Wiratunga, Nirmalie
Palihawadana, Chamath
Nkisi-Orji, Ikechukwu
Corsar, David
Díaz-Agudo, Belén
Recio-García, Juan A.
Caro-Martínez, Marta
Bridge, Derek G.
Journal Title
Journal ISSN
Volume Title
Publisher
CEUR-WS
Published Version
Abstract
The right to an explanation of the decision reached by a machine learning (ML) model is now an EU regulation. However, different system stakeholders may have different background knowledge, competencies and goals, thus requiring different kinds of explanations. There is a growing armoury of XAI methods, interpreting ML models and explaining their predictions, recommendations and diagnoses. We refer to these collectively as ”explanation strategies”. As these explanation strategies mature, practitioners gain experience in understanding which strategies to deploy in different circumstances. What is lacking, and what the iSee project will address, is the science and technology for capturing, sharing and re-using explanation strategies based on similar user experiences, along with a much-needed route to explainable AI (XAI) compliance. Our vision is to improve every user’s experience of AI, by harnessing experiences of best practice in XAI by providing an interactive environment where personalised explanation experiences are accessible to everyone.
Video Link: https://youtu.be/81O6-q_yx0s
Description
Keywords
Explainability , Case-based reasoning , Project showcase
Citation
Martin, K., Wijekoon, A., Wiratunga, N., Palihawadana, C., Nkisi-Orji, I., Corsar, D., Díaz-Agudo, B., Recio-García, J. A., Caro-Martínez, M., Bridge, D. and Pradeep, P. (2022) 'iSee: intelligent sharing of explanation experiences', in Reuss, P. and Schönborn, J. (eds.) Workshop proceedings of the 30th International conference on case-based reasoning (ICCBR-WS 2022), 12-15 September 2022, Nancy, France. CEUR Workshop Proceedings, 3389. Aachen: CEUR-WS [online], pp. 231-232. Available at: https://ceur-ws.org/Vol-3389/ICCBR_2022_Workshop_paper_83.pdf