XAI & I: Self-explanatory AI facilitating mutual understanding between AI and human experts

Grange, Jacques A. ORCID: https://orcid.org/0000-0001-5197-249X, Princis, Henrijs, Kozlowski, Theodor R. W., Amadou-Dioffo, Aissa, Wu, Jing ORCID: https://orcid.org/0000-0001-5123-9861, Hicks, Yulia A. ORCID: https://orcid.org/0000-0002-7179-4587 and Johansen, Mark K. ORCID: https://orcid.org/0000-0001-6429-1976 2022. XAI & I: Self-explanatory AI facilitating mutual understanding between AI and human experts. Presented at: 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022), 7-9 September 2022. Procedia Computer Science. Elsevier, 10.1016/j.procs.2022.09.419

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Abstract

Traditionally, explainable artificial intelligence seeks to provide explanation and interpretability of high-performing black-box models such as deep neural networks. Interpretation of such models remains difficult, because of their high complexity. An alternative method is to instead force a deep-neural network to use human-intelligible features as the basis for its decisions. We tested this approach using the natural category domain of rock types. We compared the performance of a black-box implementation of transfer-learning using Resnet50 to that of a network first trained to predict expert-identified features and then forced to use these features to categorise rock images. The performance of this feature-constrained network was virtually identical to that of the unconstrained network. Further, a partially constrained network forced to condense down to a small number of features that was not trained with expert features did not result in these abstracted features being intelligible; nevertheless, an affine transformation of these features could be found that aligned well with expert-intelligible features. These findings show that making an AI intrinsically intelligible need not be at the cost of performance.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Psychology Computer Science & Informatics
Publisher:	Elsevier
ISSN:	1877-0509
Date of First Compliant Deposit:	18 October 2022
Date of Acceptance:	30 September 2022
Last Modified:	26 Jan 2023 22:27
URI:	https://orca.cardiff.ac.uk/id/eprint/153482

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