Explainable AI (XAI) has garnered attention in recent news due to its significance in ensuring transparency and interpretability in artificial intelligence systems. The concept of XAI has become increasingly relevant as AI technologies are integrated into various aspects of society, raising concerns about the lack of understanding behind AI decision-making processes. This has led to discussions on the importance of developing AI systems that can provide explanations for their outputs, particularly in critical applications such as healthcare, finance, and autonomous vehicles.
XAI refers to a set of methods and techniques designed to make AI systems more interpretable to humans, enabling users to understand how AI algorithms arrive at their conclusions. By providing explanations for AI decisions, XAI aims to enhance trust, accountability, and fairness in AI applications. This approach stands in contrast to traditional "black box" AI models, which operate without clear visibility into their inner workings. As the demand for responsible AI grows, XAI has emerged as a key area of research and development in the field of artificial intelligence.