Artificial intelligence (AI), particularly deep learning models, are often considered black boxes because their decision-making processes remain difficult to interpret. These models can accurately identify objects—such as recognizing a bird in a photo—but understanding exactly how they arrive at these conclusions is a significant challenge. Until now, most interpretability efforts have focused on analyzing the internal structures of the models themselves.