Explainable AI

Current research on explainability and interpretability of machine learning algorithms

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On the Robustness of Interpretability Methods

David Alvarez-Melis, Tommi S. Jaakkola. 2018

[ArXiv]    

We argue that robustness of explanations—i.e., that similar inputs should give rise to similar explanations—is a key desideratum for interpretability. We introduce metrics to quantify robustness and demonstrate that current methods do not perform well according to these metrics. Finally, we propose ways that robustness can be enforced on existing interpretability approaches.