XAI-Exit: Interpretability-Driven Dynamic Early Exits for Efficient and Transparent DNN Inference

Deep neural networks (DNNs) excel across domains but face challenges in resource-constrained and critical settings due to high computational cost and limited transparency. Early exit DNNs reduce overhead via intermediate predictions; yet, most approaches neglect interpretability, vital for trust in AI systems. This article presents XAI-Exit, an early exit framework that jointly optimizes efficiency and transparency. At its core, ExitDecisionNet (EDN)-a lightweight RNN trained with a curriculum strategy on confidence, interpretability, and stability metrics-dynamically predicts the optimal exit, while a skip mechanism minimizes redundant computation. To ensure transparency, exit attribution maps (EAMs) aggregate feature attributions across exits, revealing the decision trajectory and are complemented by standard XAI methods (integrated gradients (IGs), SmoothGrad, Grad-CAM++, and LRP). Experiments on MobileNetV3, ResNet18, and MSDNet with CIFAR-10, CIFAR-100, and ImageNet show that XAI-Exit improves efficiency without sacrificing accuracy, while uniquely ensuring interpretable exit decisions suitable for real-world deployment.