Asian Research Journal of Current Science

Deep learning systems may experience performance degradation when deployed under distribution shifts, noisy data, and changing operational conditions. Furthermore, explanations generated by explainable artificial intelligence (XAI) methods may become unstable or unreliable under such conditions, limiting the interpretability and trustworthiness of model predictions. This paper proposes a conceptual Robust Explainable Deep Learning (R-XDL) framework that integrates data-quality assessment, distribution-shift and noise diagnostics, robust prediction, uncertainty estimation, explanation fidelity and stability assessment, risk-aware decision-making, human oversight, and controlled model adaptation. A conceptual mathematical objective and a risk-scoring mechanism are introduced to organise predictive robustness and explanation reliability within a unified lifecycle. The framework also proposes an explanation diagnostic gate that uses fidelity, stability, predictive uncertainty, and distribution-shift indicators to support decisions to accept a prediction, request human review, or defer automated decision-making. In addition, a proposed empirical evaluation protocol outlines the use of benchmark datasets, controlled corruption scenarios, appropriate baseline methods, calibration measures, explanation metrics, computational-cost analysis, and ablation studies. This manuscript presents a conceptual framework and proposed evaluation methodology; no experiments have been conducted, and no empirical performance results are claimed. Future research will implement and evaluate the framework to determine its effectiveness, limitations, and practical applicability. The proposed work provides a structured foundation for investigating the joint reliability of deep learning predictions and explanations under distribution shifts and noisy data.