Focus! Rating XAI Methods and Finding Biases
Anna Arias-Duart, Ferran Par\'es, Dario Garcia-Gasulla, Victor, Gimenez-Abalos

TL;DR
This paper introduces the Focus score, an evaluation metric for feature attribution methods in image recognition, assessing their coherency and bias, and compares popular explainability techniques across CNN models.
Contribution
The paper proposes a novel evaluation score, Focus, for assessing feature attribution methods within the distribution, and demonstrates its use in comparing explainability techniques and identifying model biases.
Findings
LRP and GradCAM are consistently reliable explanations.
SmoothGrad and IG tend to produce class-agnostic explanations.
Focus effectively identifies and characterizes biases in models.
Abstract
AI explainability improves the transparency of models, making them more trustworthy. Such goals are motivated by the emergence of deep learning models, which are obscure by nature; even in the domain of images, where deep learning has succeeded the most, explainability is still poorly assessed. In the field of image recognition many feature attribution methods have been proposed with the purpose of explaining a model's behavior using visual cues. However, no metrics have been established so far to assess and select these methods objectively. In this paper we propose a consistent evaluation score for feature attribution methods -- the Focus -- designed to quantify their coherency to the task. While most previous work adds out-of-distribution noise to samples, we introduce a methodology to add noise from within the distribution. This is done through mosaics of instances from different…
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Code & Models
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Adversarial Robustness in Machine Learning · Advanced Neural Network Applications
MethodsResidual Connection · Batch Normalization · 1x1 Convolution · Average Pooling · Grouped Convolution · *Communicated@Fast*How Do I Communicate to Expedia? · Dropout · Local Response Normalization · Softmax · Dense Connections
