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.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.Computer Sciences[#item_full_content]