Interpretability is the degree to which a human can understand the cause of a decision made by a model or system, crucial for trust and accountability in AI and machine learning applications. It enables stakeholders to validate models, ensure fairness, and comply with regulatory standards by providing insights into how inputs are transformed into outputs.