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Authors: Yuhao Zhang 1 ; 2 ; Kevin McAreavey 1 and Weiru Liu 1

Affiliations: 1 Department of Engineering Mathematics, University of Bristol, U.K. ; 2 Tencent AI Lab, Shanghai, China

Keyword(s): Explainable AI, Explainable Machine Learning, Global And Local Explanations, Counterfactual Explanations.

Abstract: There has been a sharp rise in research activities on explainable artificial intelligence (XAI), especially in the context of machine learning (ML). However, there has been less progress in developing and implementing XAI techniques in AI-enabled environments involving non-expert stakeholders. This paper reports our investigations into providing explanations on the outcomes of ML algorithms to non-experts. We investigate the use of three explanation approaches (global, local, and counterfactual), considering decision trees as a use case ML model. We demonstrate the approaches with a sample dataset, and provide empirical results from a study involving over 200 participants. Our results show that most participants have a good understanding of the generated explanations.

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Paper citation in several formats:
Zhang, Y.; McAreavey, K. and Liu, W. (2022). Developing and Experimenting on Approaches to Explainability in AI Systems. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 518-527. DOI: 10.5220/0010900300003116

@conference{icaart22,
author={Yuhao Zhang. and Kevin McAreavey. and Weiru Liu.},
title={Developing and Experimenting on Approaches to Explainability in AI Systems},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2022},
pages={518-527},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010900300003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Developing and Experimenting on Approaches to Explainability in AI Systems
SN - 978-989-758-547-0
IS - 2184-433X
AU - Zhang, Y.
AU - McAreavey, K.
AU - Liu, W.
PY - 2022
SP - 518
EP - 527
DO - 10.5220/0010900300003116
PB - SciTePress

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