Sanity Checks for Saliency Maps

TL;DR

This paper introduces a methodology to evaluate saliency maps, revealing that many existing methods are unreliable and independent of models or data, which limits their usefulness for critical tasks.

Contribution

The authors propose an actionable evaluation framework for saliency methods, demonstrating that many are inadequate for tasks requiring data or model sensitivity.

Findings

Some saliency methods are independent of the model and data

Visual assessment alone can be misleading

Methods failing the tests are unsuitable for sensitive tasks

Abstract

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.