VALE: A Multimodal Visual and Language Explanation Framework for Image Classifiers using eXplainable AI and Language Models

Bridging the "semantic gap" improves AI transparency: VALE addresses a fundamental challenge in explainable AI by combining visual explanations (highlighting which parts of an image influenced a decision) with natural language descriptions, making AI reasoning more understandable to non-technical users rather than relying on technical visualizations alone.

Black box AI poses real risks in high-stakes applications: The paper emphasizes that the opacity of deep neural networks currently limits their safe deployment in critical domains like healthcare, finance, and criminal justice—a key motivation for developing better explanation methods like VALE.

Multiple explanation formats serve different needs: By generating both visual and textual explanations simultaneously, VALE recognizes that different stakeholders may need different types of information to understand and trust AI decisions, rather than assuming one explanation format fits all users.

Current XAI methods face inherent trade-offs: The framework acknowledges that existing explainability techniques struggle with balancing interpretability against model performance, suggesting that achieving truly transparent AI requires ongoing technical innovation rather than simple solutions.

Language models can translate visual AI decisions into human language: VALE demonstrates that advanced language models can convert technical visual explanations (like SHAP visualizations) into plain-language descriptions, potentially making AI auditing and oversight more accessible to non-specialists.