Unlocking the Black Box: Concept-Based Modeling for Interpretable Affective Computing Applications

Black-box Machine Learning (ML) models are widely used in Affective Computing applications, including in high-stake tasks, such as autonomous driving or mental health applications. Despite their state-of-the-art performance, black-box models are usually criticized as non-interpretable as they only produce a final prediction without providing insight into the decision-making process. Previous research on explainable affective computing applications mainly focuses on post-hoc techniques or reverting to traditional ML approaches, which may compromise either the explainability or the performance. Recently, concept-based models have demonstrated great success in general object classification tasks by training the ML model to learn both the classification label as well as the underlying concepts that contribute to the prediction. However, the experimental scenario is different in affective computing applications, for example, facial expression recognition or emotion detection, where it is challenging to select meaningful concepts because of the inherent uncertainty of the tasks. In this paper, we propose a novel concept-based modeling framework for Affective Computing applications, using Facial Expression Recognition (FER) as a benchmark example. Our work is the first to present a Concept-based FER approach and explore concept selection for this application area leveraging concept-based models, including Concept Bottleneck Models (CBMs) and Concept Embedding Models (CEMs). Experimental evaluation on two benchmark datasets: Aff-Wild2 and AffectNet, shows that our proposed framework provides robust explanations for each prediction while maintaining competitive performance compared to state-of-the-art black-box models. We believe the proposed framework paves the way for future interpretable investigations into downstream applications in human behavior understanding.