LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks

Textual explanations bridge the AI knowledge gap: LangXAI addresses a critical problem where end-users without AI expertise struggle to understand visual AI decisions. By converting technical model outputs into plain language explanations, the framework makes AI systems more accessible to non-specialists who need to trust and use these systems.

The framework works across multiple vision tasks: LangXAI demonstrates effectiveness not just for image classification, but also for more complex tasks like object detection and semantic segmentation, suggesting the approach has broad applicability across different types of visual AI systems that organizations deploy.

Quality explanations are measurable: The paper reports "high BERTScore" results, indicating the generated textual explanations are linguistically coherent and meaningful. This suggests the framework produces reliable explanations rather than generic or misleading descriptions, which is essential for responsible AI deployment.

Transparency and reliability are interconnected: By generating clear textual explanations, LangXAI aims to foster "a more transparent and reliable AI framework." This reflects an important principle in responsible AI: users are more likely to trust and appropriately use AI systems when they can understand why the system made a particular decision.

Explainability remains a persistent challenge: The paper's acknowledgment that "an understanding gap persists" despite XAI advancements highlights that making AI truly explainable to end-users is an ongoing challenge requiring continued innovation beyond technical improvements alone.