Explainable AI is a research area that focuses on developing interpretable and transparent artificial intelligence models. The goal is to create AI systems that can explain their decisions and reasoning processes in a way that is easily understood by humans. This is important for various reasons, such as ensuring accountability, building trust, and identifying potential biases in AI systems. Explainable AI methods can include techniques such as feature visualization, model explanation, and interactive interfaces that allow users to interact with AI systems and understand their inner workings. Ultimately, explainable AI aims to make AI more trustworthy and accessible to a wider range of users.