Explainable AI (XAI) refers to the set of techniques and methodologies aimed at making the decisions and predictions of artificial intelligence (AI) systems understandable and interpretable by humans. As AI models become increasingly complex and are used in critical applications such as healthcare, finance, and autonomous vehicles, there is a growing need for transparency and accountability in AI decision-making processes. XAI techniques provide insights into how AI models arrive at their predictions or recommendations, allowing users to understand the factors and features that influence the model's outputs. Some common approaches to XAI include feature importance analysis, model interpretation methods, rule extraction techniques, and generating human-understandable explanations for model predictions. By enhancing the interpretability of AI systems, XAI enables users to trust AI models, identify biases or errors, and make informed decisions based on AI-generated insights.

Note: This concise definition provides an overview of Explainable AI (XAI). For further information, a more in-depth search on Google is recommended.