Towards Interpretability in Audio and Visual Affective Machine Learning: A Review
This review addresses the need for transparency in affective computing to prevent bias, but it is incremental as it synthesizes existing research without introducing new methods.
The paper reviews the use of interpretability methods in affective machine learning with audio and visual data, finding that while adoption has increased in the last five years, it remains limited in scope, depth, and application.
Machine learning is frequently used in affective computing, but presents challenges due the opacity of state-of-the-art machine learning methods. Because of the impact affective machine learning systems may have on an individual's life, it is important that models be made transparent to detect and mitigate biased decision making. In this regard, affective machine learning could benefit from the recent advancements in explainable artificial intelligence (XAI) research. We perform a structured literature review to examine the use of interpretability in the context of affective machine learning. We focus on studies using audio, visual, or audiovisual data for model training and identified 29 research articles. Our findings show an emergence of the use of interpretability methods in the last five years. However, their use is currently limited regarding the range of methods used, the depth of evaluations, and the consideration of use-cases. We outline the main gaps in the research and provide recommendations for researchers that aim to implement interpretable methods for affective machine learning.