  The ability for a human to understand an Artificial Intelligence (AI) model's
decision-making process is critical in enabling stakeholders to visualize model
behavior, perform model debugging, promote trust in AI models, and assist in
collaborative human-AI decision-making. As a result, the research fields of
interpretable and explainable AI have gained traction within AI communities as
well as interdisciplinary scientists seeking to apply AI in their subject
areas. In this paper, we focus on advancing the state-of-the-art in
interpreting multimodal models - a class of machine learning methods that
tackle core challenges in representing and capturing interactions between
heterogeneous data sources such as images, text, audio, and time-series data.
Multimodal models have proliferated numerous real-world applications across
healthcare, robotics, multimedia, affective computing, and human-computer
interaction. By performing model disentanglement into unimodal contributions
(UC) and multimodal interactions (MI), our proposed approach, DIME, enables
accurate and fine-grained analysis of multimodal models while maintaining
generality across arbitrary modalities, model architectures, and tasks. Through
a comprehensive suite of experiments on both synthetic and real-world
multimodal tasks, we show that DIME generates accurate disentangled
explanations, helps users of multimodal models gain a deeper understanding of
model behavior, and presents a step towards debugging and improving these
models for real-world deployment. Code for our experiments can be found at
https://github.com/lvyiwei1/DIME.
