A Novel Framework for Automated Explain Vision Model Using Vision-Language Models

Explainability has been overlooked in vision AI development: The paper highlights that most computer vision models prioritize performance metrics (accuracy, IoU, mAP) over explainability, meaning we often don't understand why these models make their decisions—a critical gap for responsible AI deployment.

Understanding model behavior at scale matters for bias detection: The research emphasizes that analyzing how vision models behave across large datasets—not just individual images—is essential for identifying biased patterns and trends that could lead to unfair or discriminatory outcomes in real-world applications.

Vision-Language Models offer a practical solution: The paper proposes using Vision-Language Models as a tool to automatically explain vision models at both individual and dataset levels, making explainability analysis more accessible and requiring "minimal effort" compared to existing methods.

Discovering failure cases is now more feasible: By integrating explainability analysis directly into model development, the framework helps teams identify where and why their models fail, enabling earlier intervention to prevent problematic deployments.

Explainability should be built into development, not added later: The paper advocates for integrating xAI analysis throughout the vision model development process rather than treating it as an afterthought, supporting a more responsible approach to AI system design.