arXiv:2603.13326v2 Announce Type: replace Abstract: Multimodal Transformers often produce predictions without clarifying how different modalities jointly support a decision. Most existing multimodal explainable AI (MXAI) methods extend unimodal saliency to multimodal backbones, highlighting important tokens or patches within each modality, but they rarely pinpoint which cross-modal feature pairs provide complementary evidence (synergy) or serve as reliable backups (redundancy). We present Feature-level I2MoE (FL-I2MoE), a structured Mixture-of-Experts layer that operates directly on token/patc
The rapid advancement and deployment of multimodal AI models necessitate more robust explainability techniques to ensure trust and reliability in their decisions.
Improved explainability in multimodal AI will be crucial for debugging, auditing, and building confidence in autonomous systems, especially as they integrate into critical applications.
New methods are emerging that move beyond token-level saliency to explain how different modalities interact at a feature-level, offering deeper insights into AI decision-making.
- · AI developers
- · AI researchers
- · industries deploying multimodal AI
- · developers of black-box AI systems without explainability
Multimodal AI systems will become more transparent regarding their decision-making processes.
Increased transparency will accelerate the adoption of multimodal AI in sensitive or high-stakes domains.
Broader adoption of explainable multimodal AI could lead to new regulatory frameworks emphasizing transparency and accountability in AI.
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Read at arXiv cs.LG