Explainable AI

Current research on explainability and interpretability of machine learning algorithms

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Towards a Unified Evaluation of Explanation Methods without Ground Truth

Hao Zhang, Jiayi Chen, Haotian Xue, Quanshi Zhang. 2019

[ArXiv]    

This paper proposes a set of criteria to evaluate the objectiveness of explanation methods of neural networks, which is crucial for the development of explainable AI, but it also presents significant challenges. The core challenge is that people usually cannot obtain ground-truth explanations of the neural network. To this end, we design four metrics to evaluate explanation results without ground-truth explanations. Our metrics can be broadly applied to nine benchmark methods of interpreting neural networks, which provides new insights of explanation methods.