MIT and IBM researchers have introduced “saliency cards,” a tool designed to assist users in selecting appropriate saliency methods for machine learning models. These cards outline the functionalities and performance characteristics of various saliency methods, which explain complex model behaviors critical for interpreting predictions accurately, especially in real-world applications like diagnosing diseases from X-rays. With numerous saliency methods available, users can benefit from a standardized resource to compare and select the best approach tailored to their specific tasks. The cards summarize significant attributes that capture how saliency is calculated and how it relates to the model and user perception. For instance, concerns about dependency on hyperparameters are addressed, which could lead to misleading results if not understood correctly. In user studies, experts from diverse fields found these cards useful for prioritizing attributes and making informed decisions, even those unfamiliar with machine learning concepts. The researchers aim to explore less-evaluated attributes, develop tailored methods, and enhance user understanding of saliency outputs. Moving forward, they intend to maintain these cards as dynamic resources that evolve with advancements in saliency methods and evaluations, fostering a broader dialogue on their attributes and applications.
