AI Explainability in Multimodal Models Lacks Robust Evaluation

A recent systematic literature review published on arXiv examines the explainability of multimodal attention-based AI models. The review, covering research from January 2020 to early 2024, found that most studies focus on vision-language and language-only models, frequently employing attention-based techniques for explanations. However, these methods often struggle to fully capture inter-modal interactions, and current evaluation practices for multimodal explainability lack consistency and robustness. The authors propose recommendations to foster more rigorous and standardized evaluation in this field to promote responsible AI development. AI

IMPACT Highlights a critical need for standardized evaluation methods in multimodal AI explainability to ensure more interpretable and accountable systems.

RANK_REASON The cluster is a systematic literature review published on arXiv, which falls under the research category. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Md Raisul Kibria

• paper
• safety

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