Sanity Checks for Saliency Maps
This work addresses the need for reliable interpretability tools in machine learning, particularly for researchers and practitioners using saliency maps, and is incremental in proposing a new evaluation methodology.
The authors tackled the problem of evaluating saliency map methods for model interpretability, finding that some existing methods are independent of both the model and data, making them inadequate for tasks like debugging or outlier detection.
Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model. Several saliency methods have been proposed, often guided by visual appeal on image data. In this work, we propose an actionable methodology to evaluate what kinds of explanations a given method can and cannot provide. We find that reliance, solely, on visual assessment can be misleading. Through extensive experiments we show that some existing saliency methods are independent both of the model and of the data generating process. Consequently, methods that fail the proposed tests are inadequate for tasks that are sensitive to either data or model, such as, finding outliers in the data, explaining the relationship between inputs and outputs that the model learned, and debugging the model. We interpret our findings through an analogy with edge detection in images, a technique that requires neither training data nor model. Theory in the case of a linear model and a single-layer convolutional neural network supports our experimental findings.