Explainable Artificial Intelligence: Methods, Challenges, and Future Directions

Artificial intelligence (AI) systems are increasingly deployed in high-stakes domains, raising concerns over transparency, fairness, and accountability. Explainable artificial intelligence (XAI) aims to address these challenges by providing human-interpretable insights into AI models. This paper reviews the current landscape of XAI methods, highlights their advantages and limitations, and outlines future research directions for trustworthy AI deployment.