We describe a novel attribution method which is grounded in Sensitivity Analysis and uses Sobol indices. Beyond modeling the individual contributions of image regions, Sobol indices provide an efficient way to capture higher-order interactions between image regions and their contributions to a neural network's prediction through the lens of variance. We describe an approach that makes the computation of these indices efficient for high-dimensional problems by using perturbation masks coupled with efficient estimators to handle the high dimensionality of images. Importantly, we show that the proposed method leads to favorable scores on standard benchmarks for vision (and language models) while drastically reducing the computing time compared to other black-box methods -even surpassing the accuracy of state-of-the-art white-box methods which require access to internal representations. Our code is freely available: github.com/fel-thomas/ Sobol-Attribution-Method.* Equal contribution † Work done before April 2021 and joining Tesla Preprint. Under review.
A multitude of explainability methods and theoretical evaluation scores have been proposed. However, it is not yet known: (1) how useful these methods are in real-world scenarios and (2) how well theoretical measures predict the usefulness of these methods for practical use by a human. To fill this gap, we conducted human psychophysics experiments at scale to evaluate the ability of human participants (n = 1, 150) to leverage representative attribution methods to learn to predict the decision of different image classifiers. Our results demonstrate that theoretical measures used to score explainability methods poorly reflect the practical usefulness of individual attribution methods in real-world scenarios. Furthermore, the degree to which individual attribution methods helped human participants predict classifiers' decisions varied widely across categorization tasks and datasets.Overall, our results highlight fundamental challenges for the field -suggesting a critical need to develop better explainability methods and to deploy human-centered evaluation approaches. We will make the code of our framework available to ease the systematic evaluation of novel explainability methods.
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