Towards the development of explainable machine learning models to recognize the faces of autistic children: a brief report

Abstract

Purpose Machine learning with image classification has shown promise in supporting the detection of autism in children, but the development of explainable models is still lacking. To address this issue, the purpose of this study was to compare the development of explainable models using two different algorithms to identify the facial features that deep neural networks used to classify children as autistic or non-autistic. Design/methodology/approach First, this paper trained and tested different models on the Autistic Children Facial Image Data Set and selected the one that produced the highest accuracy. Following the identification of the best model, the analyses compared two methods to examine explainability: Local Interpretable Model-agnostic Explanations and Randomized Input Sampling for Explanation of black-box models. Findings Overall, the best model, ViT_Huge_14, produced an accuracy of 92%. Moreover, Local Interpretable Model-agnostic Explanations resulted in more explainable models than Randomized Input Sampling for Explanation of black-box models. Albeit promising, researchers must conduct further studies to examine the generalizability of the results and consider ethical issues before recommending facial image classification as a component of a multimethod approach to screening and diagnosis. Originality/value To the best of the authors’ knowledge, this study is the first to examine the development of explainable models to detect autism using facial features.

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