Who Explains the Explanation? Quantitatively Assessing Feature
Attribution Methods
- URL: http://arxiv.org/abs/2109.15035v1
- Date: Tue, 28 Sep 2021 07:10:24 GMT
- Title: Who Explains the Explanation? Quantitatively Assessing Feature
Attribution Methods
- Authors: Anna Arias-Duart, Ferran Par\'es and Dario Garcia-Gasulla
- Abstract summary: We propose a novel evaluation metric -- the Focus -- designed to quantify the faithfulness of explanations.
We show the robustness of the metric through randomization experiments, and then use Focus to evaluate and compare three popular explainability techniques.
Our results find LRP and GradCAM to be consistent and reliable, while the latter remains most competitive even when applied to poorly performing models.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: AI explainability seeks to increase the transparency of models, making them
more trustworthy in the process. The need for transparency has been recently
motivated by the emergence of deep learning models, which are particularly
obscure by nature. Even in the domain of images, where deep learning has
succeeded the most, explainability is still poorly assessed. Multiple feature
attribution methods have been proposed in the literature with the purpose of
explaining a DL model's behavior using visual queues, but no standardized
metrics to assess or select these methods exist. In this paper we propose a
novel evaluation metric -- the Focus -- designed to quantify the faithfulness
of explanations provided by feature attribution methods, such as LRP or
GradCAM. First, we show the robustness of the metric through randomization
experiments, and then use Focus to evaluate and compare three popular
explainability techniques using multiple architectures and datasets. Our
results find LRP and GradCAM to be consistent and reliable, the former being
more accurate for high performing models, while the latter remains most
competitive even when applied to poorly performing models. Finally, we identify
a strong relation between Focus and factors like model architecture and task,
unveiling a new unsupervised approach for the assessment of models.
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