Causal Cross-Modal Attention for Interpretable Multimodal Decision Making

Multimodal decision-making systems, which integrate information from diverse data sources like text, images, and audio, have shown significant promise across various domains. However, a fundamental challenge persists: accurately identifying genuinely causal relationships between modalities rather than relying on spurious correlations, and subsequently ensuring the interpretability of the decision-making process. This paper proposes a novel framework centered on Causal Cross-Modal Attention, designed to enhance the accuracy and transparency of multimodal systems. By explicitly modeling causal links between different data modalities, this approach aims to mitigate the influence of confounding factors and spurious associations that often plague traditional fusion techniques. The proposed methodology integrates principles from causal inference with advanced attention mechanisms, allowing the model to learn and highlight the direct influence of one modality's features on another, as well as their collective impact on the final decision. This enables not only improved predictive performance but also provides actionable insights into why a particular decision was made, attributing contributions causally to specific modal inputs. We discuss the theoretical underpinnings, potential architectural designs, and the anticipated benefits of such a framework, emphasizing its role in fostering robust, reliable, and interpretable multimodal artificial intelligence for critical applications.