Explainable Artificial Intelligence (XAI) addresses the opacity of complex machine learning models, ensuring transparency, trust, and accountability in critical applications. This survey reviews XAI techniques, categorized into modelagnostic and model-specific approaches, alongside tools, frame- works, stakeholder perspectives, and emerging technologies. It explores their theoretical foundations, practical applications in healthcare, finance, autonomous systems, legal systems, education, cybersecurity, smart cities, robotics, agriculture, IoT systems, human-AI collaboration, ethical AI, and environmental monitoring, and recent case studies (20232025). The paper examines evaluation metrics, frameworks, ethical considerations, standardization efforts, implementation challenges, and future directions, emphasizing the balance between performance and interpretability. By synthesizing advancements and identifying open problems, this work serves as a vital resource for researchers and practitioners advancing trustworthy AI systems at institutions like PES University.
Kohli Neha (Thu,) studied this question.