Example Concept: Explainable AI involves creating models that provide clear insights into their decision-making processes. This can include visualizations of how input features contribute to outputs, or simplified models that approximate the behavior of complex systems. The goal is to make AI decisions interpretable by humans, enhancing trust and enabling users to understand the rationale behind AI predictions.