A Comparative Study of Explainable AI Methods: Model-Agnostic vs. Model-Specific Approaches

There is no single best explanation method for AI systems. The paper finds that model-agnostic methods like LIME and SHAP and model-specific methods like Grad-CAM each reveal different aspects of how AI makes decisions, meaning organizations need to choose based on their specific needs rather than relying on one approach.

Different XAI methods have different trade-offs. Model-agnostic techniques work across different AI architectures and provide broader insights, while model-specific approaches are faster computationally but only work with particular types of models—a practical consideration for real-world deployment.

Using multiple explanation methods together provides better transparency. The paper recommends combining different XAI techniques to get a more complete picture of how an AI model works, which is especially important in high-stakes applications like healthcare, autonomous vehicles, and financial services where understanding AI decisions is critical.

Explainability methods should be selected based on application requirements. Rather than adopting one standard approach, organizations should match their choice of explanation technique to their specific needs—whether they prioritize speed, broad compatibility across systems, or precise identification of which parts of an image influenced a decision.