Combining Counterfactuals With Shapley Values To Explain Image Models

• URL: http://arxiv.org/abs/2206.07087v1
• Date: Tue, 14 Jun 2022 18:23:58 GMT
• Title: Combining Counterfactuals With Shapley Values To Explain Image Models
• Authors: Aditya Lahiri, Kamran Alipour, Ehsan Adeli, Babak Salimi
• Abstract summary: We develop a pipeline to generate counterfactuals and estimate Shapley values. We obtain contrastive and interpretable explanations with strong axiomatic guarantees.
• Score: 13.671174461441304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the widespread use of sophisticated machine learning models in sensitive applications, understanding their decision-making has become an essential task. Models trained on tabular data have witnessed significant progress in explanations of their underlying decision making processes by virtue of having a small number of discrete features. However, applying these methods to high-dimensional inputs such as images is not a trivial task. Images are composed of pixels at an atomic level and do not carry any interpretability by themselves. In this work, we seek to use annotated high-level interpretable features of images to provide explanations. We leverage the Shapley value framework from Game Theory, which has garnered wide acceptance in general XAI problems. By developing a pipeline to generate counterfactuals and subsequently using it to estimate Shapley values, we obtain contrastive and interpretable explanations with strong axiomatic guarantees.

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