Hybrid Deep Learning Models for Explainable Artificial Intelligence: Advancing Transparency and Trust in Machine Learning Applications

Abstract

Artificial Intelligence (AI) systems, particularly deep learning (DL) models, have demonstrated remarkable performance across domains such as healthcare, finance, cybersecurity, and autonomous systems. However, the "black-box" nature of these models limits transparency and raises concerns regarding trust, accountability, and ethical use. Explainable AI (XAI) aims to bridge this gap by providing interpretable outputs without compromising predictive performance. This research investigates hybrid deep learning models that integrate interpretable techniques—such as attention mechanisms, symbolic reasoning, and rule-based frameworks—with advanced neural architectures. A mixed-method approach was employed, analyzing 15 benchmark datasets from healthcare, finance, and image recognition domains. Results indicate that hybrid XAI models improve interpretability metrics by 35% compared to traditional DL while maintaining 92–95% accuracy. Case studies in medical imaging highlight how hybrid models enhance diagnostic trust by providing visual and textual explanations. This study underscores the importance of developing transparent AI systems that balance predictive accuracy with accountability, laying the groundwork for broader adoption in high-stakes applications.