Explainable AI for Audio and Visual Affective Computing: A Scoping Review

Abstract

Affective computing often relies on audiovisual data to identify affective states from non-verbal signals, such as facial expressions and vocal cues. Since automatic affect recognition can be used in sensitive applications, such as healthcare and education, it is crucial to understand how models arrive at their decisions. Interpretability of machine learning models is the goal of the emerging research area of Explainable AI (explainable AI (XAI)). This scoping review aims to survey the field of audiovisual affective machine learning to identify how XAI is applied in this domain. We first provide an overview of XAI concepts relevant to affective computing. Next, following the recommended PRISMA guidelines, we perform a literature search in the ACM, IEEE, Web of Science and PubMed databases. After systematically reviewing 1190 articles, a final set of 65 papers is included in our analysis. We quantitatively summarize the scope, methods and evaluation of the XAI techniques used in the identified papers. Our findings show encouraging developments for using XAI to explain models in audiovisual affective computing, yet only a limited set of methods are used in the reviewed works. Following a critical discussion, we provide recommendations for incorporating interpretability in future work for affective machine learning.