While Deep Neural Networks (DNNs) achieve state-of-the-art performance across various domains, their inherent lack of interpretability hinders their deployment in high-stakes, safety-critical applications. To bridge this gap, this paper proposes HT-DNN, a novel hybrid Explainable Artificial Intelligence (XAI) framework. HT-DNN uniquely integrates local surrogate explanations for instance-level transparency, global concept-based reasoning for macro-level understanding, and an automated accountability auditing module to detect spurious correlations. Experimental evaluations on the CIFAR-10 benchmark dataset demonstrate that HT-DNN significantly improves explanation fidelity and interpretability without compromising the underlying model's predictive accuracy.