Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models
arXiv:2508.04427v2 Announce Type: replace-cross Abstract: Multimodal learning has witnessed remarkable advancements in recent years, particularly with the integration of attention-based models, leading to significant performance gains across a variety of tasks. Parallel to this progress, the demand for explainable artificial intelligence (XAI) has spurred a growing body of research aimed at interpreting the complex decision-making processes of these models. This systematic literature review analyzes research published between January 2020 and early 2024 that focuses on the explainability of mu
The rapid advancement of multimodal AI models necessitates a parallel focus on explainability, as their complexity increases the 'black box' problem, especially for critical applications. This review consolidates progress and identifies gaps in a rapidly evolving field.
Explainable AI (XAI) for multimodal models is crucial for building trust, enabling regulatory compliance, and facilitating robust debugging and improvement of complex AI systems across various industries. Without it, adoption in high-stakes domains will be constrained.
Increased focus on multimodal explainability promises to accelerate the deployment of these powerful AI systems into sensitive applications by making their decision-making processes more transparent and auditable.
- · AI developers
- · Regulatory bodies
- · Industries adopting multimodal AI (e.g., healthcare, defense)
- · Auditors and ethicists
- · Developers neglecting XAI practices
- · Proprietary black-box AI models in regulated sectors
Systematic understanding of XAI approaches for multimodal models will lead to more standardized development practices.
Improved explainability will accelerate the adoption of multimodal AI in safety-critical and regulated applications, broadening its economic impact.
Enhanced trust in multimodal AI could lead to its integration into autonomous decision-making systems with significant societal implications, potentially reducing human oversight in certain domains.
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Read at arXiv cs.AI