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Authors: Kohei Asano and Jinhee Chun

Affiliation: Graduate School of Information Sciences, Tohoku University, Sendai, Japan

Keyword(s): Explanations, Interpretability, Transparency.

Abstract: We propose a novel global explanation method for a pre-trained machine learning model. Generally, machine learning models behave as a black box. Therefore, developing a tool that reveals a model’s behavior is important. Some studies have addressed this issue by approximating a black-box model with another interpretable model. Although such a model summarizes a complex model, it sometimes provides incorrect explanations because of a gap between the complex model. We define hypersphere sets of two types that respectively approximate a model based on recall and precision metrics. A high-recall set of hyperspheres provides a summary of a black-box model. A high-precision one describes the model’s behavior precisely. We demonstrate from experimentation that the proposed method provides a global explanation for an arbitrary black-box model. Especially, it improves recall and precision metrics better than earlier methods.

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Paper citation in several formats:
Asano, K. and Chun, J. (2022). Post-hoc Global Explanation using Hypersphere Sets. 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 236-243. DOI: 10.5220/0010819100003116

@conference{icaart22,
author={Kohei Asano. and Jinhee Chun.},
title={Post-hoc Global Explanation using Hypersphere Sets},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2022},
pages={236-243},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010819100003116},
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 - Post-hoc Global Explanation using Hypersphere Sets
SN - 978-989-758-547-0
IS - 2184-433X
AU - Asano, K.
AU - Chun, J.
PY - 2022
SP - 236
EP - 243
DO - 10.5220/0010819100003116
PB - SciTePress

- Science and Technology Publications, Lda.
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