Deep learning models learn high-dimensional, non-linear representations through layered parameterization that resists simple causal narratives. The internal mechanisms (e.g., distributed feature representations and complex statistical transformations) are difficult to map to human- interpretable rules, making explanation challenging. This is the primary reason for explainability difficulty in deep learning.