Decoding the Black Box: A Comprehensive Review of Explainable Artificial Intelligence

This review explores the current state of Explainable Artificial Intelligence (XAI). This study looks at current advances in XAI research, as well as challenges and the future. To accomplish this, the review will explore XAI literature on a wide range of topics, including current techniques, strategies, and applications. The review begins with an overview of XAI, including its definition, history, and motivations. It will then investigate XAI's current situation as well as explainability approaches. This will entail a look at model-based, post-hoc, and interactive explainability, as well as the methods used to get there, such as feature attribution, rule extraction, and counterfactual analysis. The application of XAI in healthcare, finance, and autonomous systems will be examined. The review will look at the ethical and social consequences of XAI, such as bias, accountability, and transparency. Among the challenges that XAI faces include a lack of uniform measurements and evaluation procedures, interpretable data, and domain-specific expertise. The review will include important trends, patterns, and avenues for improvement. The review will also recommend XAI research and development. This review will assist XAI researchers, practitioners, and policymakers in understanding the field's current state, limitations and challenges, and future direction.