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Christopher Anders

2 accepted papers

2020

Fairwashing explanations with off-manifold detergent

ICML 2020poster

Explanation methods promise to make black-box classifiers more transparent. As a result, it is hoped that they can act as proof for a sensible, fair and trustworthy decision-making process of the algorithm and thereby increase its acceptance by the end-users. In this paper, we show both theoreticall…

2019

Explanations can be manipulated and geometry is to blame

NeurIPS 2019poster

Explanation methods aim to make neural networks more trustworthy and interpretable. In this paper, we demonstrate a property of explanation methods which is disconcerting for both of these purposes. Namely, we show that explanations can be manipulated arbitrarily by applying visually hardly percepti…