Developing a catalogue of explainability methods to support expert and non-expert users.

Martin, Kyle; Liret, Anne; Wiratunga, Nirmalie; Owusu, Gilbert; Kern, Mathias

Abstract

Organisations face growing legal requirements and ethical responsibilities to ensure that decisions made by their intelligent systems are explainable. However, provisioning of an explanation is often application dependent, causing an extended design phase and delayed deployment. In this paper we present an explainability framework formed of a catalogue of explanation methods, allowing integration to a range of projects within a telecommunications organisation. These methods are split into low-level explanations, high-level explanations and co-created explanations. We motivate and evaluate this framework using the specific case-study of explaining the conclusions of field engineering experts to non-technical planning staff. Feedback from an iterative co-creation process and a qualitative evaluation is indicative that this is a valuable development tool for use in future company projects.

Citation

MARTIN, K., LIRET, A., WIRATUNGA, N., OWUSU, G. and KERN, M. 2019. Developing a catalogue of explainability methods to support expert and non-expert users. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence XXXVI: proceedings of the 39th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) international Artificial intelligence conference 2019 (AI 2019), 17-19 December 2019, Cambridge, UK. Lecture notes in computer science, 11927. Cham: Springer [online], pages 309-324. Available from: https://doi.org/10.1007/978-3-030-34885-4_24

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 39th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) international Artificial intelligence conference 2019 (AI 2019)
• Start Date: Dec 17, 2019
• End Date: Dec 19, 2019
• Acceptance Date: Sep 2, 2019
• Online Publication Date: Nov 19, 2019
• Publication Date: Nov 30, 2019
• Deposit Date: Sep 13, 2019
• Publicly Available Date: May 6, 2020
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 11927
• Pages: 309-324
• Series Title: Lecture notes in computer science
• Series ISSN: 0302-9743
• Book Title: Artificial intelligence XXXVI
• ISBN: 9783030348847
• DOI: https://doi.org/10.1007/978-3-030-34885-4_24
• Keywords: Machine learning; Similarity modeling: Explainability: Information retrieval
• Public URL: https://rgu-repository.worktribe.com/output/549377