EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR TRUSTWORTHY DECISION SUPPORT SYSTEMS: CHALLENGES, TECHNIQUES, AND FUTURE DIRECTIONS

Artificial Intelligence (AI) has transformed numerous sectors by enabling automated decision-making, predictive analytics, and intelligent data processing. Despite remarkable advancements in machine learning and deep learning, many AI models operate as "black boxes," making it difficult for users to understand how decisions are generated. This lack of transparency has become a major concern in critical domains such as healthcare, finance, cybersecurity, autonomous vehicles, and legal systems. Explainable Artificial Intelligence (XAI) has emerged as a promising solution to improve the interpretability, transparency, and accountability of AI systems. This paper explores the concepts, methodologies, applications, challenges, and future directions of Explainable Artificial Intelligence. It examines various explainability techniques, compares interpretable and black-box models, discusses ethical implications, and highlights recent advancements in explainable machine learning. The paper concludes that integrating explainability into AI systems is essential for building trustworthy, responsible, and human-centered intelligent systems.