Explainability refers to the ability to understand and describe how an AI model arrives at its decisions or outcomes. It’s essential for building trust, especially in critical areas like healthcare, finance, and law, where decisions can have significant consequences. Explainable AI allows users to understand the reasoning behind predictions, helping ensure that the system is working fairly and accurately. Without explainability, AI can seem like a “black box,” making it difficult to identify issues, improve systems, or ensure ethical deployment.