Explainable image classification with evidence counterfactual

• Published in: Pattern analysis and applications : PAA Vol. 25; no. 2; pp. 315 - 335
• Main Authors: Vermeire, Tom, Brughmans, Dieter, Goethals, Sofie, de Oliveira, Raphael Mazzine Barbossa, Martens, David
• Format: Journal Article
• Language: English
• Published: London Springer London 01.05.2022; Springer Nature B.V
• Subjects: Classification; Computer Science; Image classification; Literature reviews; Pattern Recognition; Prediction models; Theoretical Advances; Training; Explainable artificial intelligence; Counterfactual explanation; Search algorithms; Image classification
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-021-01055-y

The complexity of state-of-the-art modeling techniques for image classification impedes the ability to explain model predictions in an interpretable way. A counterfactual explanation highlights the parts of an image which, when removed, would change the predicted class. Both legal scholars and data scientists are increasingly turning to counterfactual explanations as these provide a high degree of human interpretability, reveal what minimal information needs to be changed in order to come to a different prediction and do not require the prediction model to be disclosed. Our literature review shows that existing counterfactual methods for image classification have strong requirements regarding access to the training data and the model internals, which often are unrealistic. Therefore, SEDC is introduced as a model-agnostic instance-level explanation method for image classification that does not need access to the training data. As image classification tasks are typically multiclass problems, an additional contribution is the introduction of the SEDC-T method that allows specifying a target counterfactual class. These methods are experimentally tested on ImageNet data, and with concrete examples, we illustrate how the resulting explanations can give insights in model decisions. Moreover, SEDC is benchmarked against existing model-agnostic explanation methods, demonstrating stability of results, computational efficiency and the counterfactual nature of the explanations.