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

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

Authors

Kyle Martin

Anne Liret

Gilbert Owusu

Mathias Kern



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.

Start Date Dec 17, 2019
Publication Date Nov 30, 2019
Publisher Springer Verlag
Pages 309-324
Series Title Lecture notes in computer science
Series Number 11927
Series ISSN 0302-9743
Book Title Artificial intelligence XXXVI
ISBN 9783030348847
Institution 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 conference on artificial intelligence (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
DOI https://doi.org/10.1007/978-3-030-34885-4_24
Keywords Machine learning; Similarity modeling: Explainability: Information retrieval

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