Disentangling Bias by Modeling Intra- and Inter-modal Causal Attention for Multimodal Sentiment Analysis
arXiv:2508.04999v2 Announce Type: replace Abstract: Multimodal sentiment analysis (MSA) aims to understand human emotions by integrating information from multiple modalities, such as text, audio, and visual data. However, existing methods often suffer from spurious correlations both within and across modalities, leading models to rely on statistical shortcuts rather than true causal relationships, thereby undermining generalization. To mitigate this issue, we propose a Multi-relational Multimodal Causal Intervention (MMCI) framework, which leverages the backdoor adjustment from causal theory t
The increasing complexity and deployment of AI models across sensitive applications necessitate robust methods to mitigate bias and ensure ethical decision-making, particularly in multimodal contexts.
Improving the causal understanding of AI models, especially in multimodal sentiment analysis, is critical for building more reliable, fair, and trustworthy AI systems that operate with less spurious correlation.
This research introduces a framework that could lead to AI models capable of better disentangling true causal relationships from mere statistical correlations, improving generalization and reducing inherent biases.
- · AI ethicists and researchers
- · Developers of multimodal AI applications
- · Industries relying on AI for decision-making (e.g., healthcare, finance)
- · Developers of AI models relying solely on statistical correlation
- · Systems prone to unmitigated bias
AI models will become more robust and trustworthy in interpreting complex human expressions.
Increased adoption of multimodal AI in sensitive domains where causal understanding is paramount.
Reduced societal harms and improved fairness in AI-driven decisions across various applications.
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