The convergence of explainable artificial intelligence and robust AI systems represents one of the most critical challenges in contemporary machine learning research. This paper presents a unified theoretical framework that addresses the fundamental tension between model interpretability and adversarial robustness, two properties traditionally viewed as conflicting objectives in AI system design. Through a comprehensive analysis of the mathematical foundations underlying both explainability and robustness, we demonstrate that these characteristics can be synergistically integrated rather than traded off against each other. The proposed framework establishes theoretical connections between explanation quality metrics and adversarial vulnerability measures, revealing that well-explained models can actually exhibit enhanced robustness when properly constructed. Our approach introduces novel methodologies for simultaneously optimizing interpretability and security in AI systems, with particular emphasis on applications in critical domains where both transparency and reliability are essential. The framework provides practical guidelines for developing AI systems that maintain high performance while offering meaningful explanations and demonstrating resilience against adversarial attacks. This work contributes to the emerging field of trustworthy AI by providing both theoretical foundations and practical methodologies for building systems that are simultaneously explainable, robust, and reliable.
Keywords: Explainable AI, Adversarial Robustness, Unified Framework, Trustworthy AI, Model Interpretability, AI Security
Citation: Massimiliano F. (2026). Unified Framework for Explainable and Robust Artificial Intelligence. J AI & Mach Lear., 2(1):1-6. DOI : https://doi.org/10.47485/3069-8006.1010