Developing and Experimenting on Approaches to Explainability in AI Systems

Yuhao Zhang, Yuhao Zhang, Kevin McAreavey, Weiru Liu

2022

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.

Paper Citation

in Harvard Style

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, pages 518-527. DOI: 10.5220/0010900300003116

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Zhang Y.
AU - McAreavey K.
AU - Liu W.
PY - 2022
SP - 518
EP - 527
DO - 10.5220/0010900300003116