arXiv:2606.14758v1 Announce Type: cross Abstract: As Vision-Language Models are increasingly deployed in safety-critical applications, the trustworthiness of their explanations becomes crucial. Explainable AI (XAI) methods for Vision-Language Models often suffer from semantic hallucination, where attribution maps highlight prominent image regions even when prompted with incorrect text descriptions (e.g., highlighting a dog when prompted ``cat''). Although this problem is widespread, a formal mathematical analysis of XAI methods and CLIP embeddings is largely missing in the literature. We demon
The increasing deployment of Vision-Language Models (VLMs) in critical applications necessitates robust interpretability to ensure trustworthiness.
Semantic hallucination in VLM explanations undermines confidence and reliability, hindering adoption in safety-critical domains such as medical diagnostics or autonomous systems.
Improved mathematical frameworks for understanding and mitigating VLM hallucination can lead to more trustworthy and deployable AI systems.
- · AI developers
- · Safety-critical AI applications
- · Explainable AI (XAI) researchers
- · End-users of AI
- · Uninterpretable AI systems
- · AI systems prone to hallucination
Further research and development in robust XAI methods will accelerate.
Increased adoption of VLMs in sensitive applications due to enhanced interpretability and trust.
New regulatory frameworks and industry standards for AI interpretability will emerge, particularly for critical systems.
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