The concept of spatial correlation in kernel normalization plays a crucial role in enhancing interpretability and explainability in deep learning models. By explicitly considering spatial relationships among input elements during normalization processes, KernelNorm enables models to capture local patterns more effectively while preserving contextual information within feature maps. This emphasis on spatial correlation enhances model transparency by highlighting how neighboring elements influence each other's representations within convolutional layers. As a result, interpreting model decisions becomes more intuitive as analysts can trace back predictions to specific regions influenced by correlated features captured through kernel normalized convolutions.