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We propose a fast, model agnostic method for finding interpretable counterfactual explanations of classifier predictions by using class prototypes.
Generating contrastive explanations with monotonic attribute functions
Luss, R., Chen, P.-Y., Dhurandhar, A., Sattigeri, P., Shanmugam, K., and Tu, C.-C · 1905
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Dhurandhar, A., Pedapati, T., Balakrishnan, A., Chen, P.-Y., Shanmugam, K., and Puri, R · 1906
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
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Explanations based on the missing: Towards contrastive explanations with pertinent negatives
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To trust or not to trust a classifier
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Comparison-based inverse classification for interpretability in machine learning
Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., and Detyniecki, M · 2018
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Counterfactual explanations without opening the black box: Automated decisions and the GDPR
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Dua, D. and Graff, C · 2017
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Protodash: fast interpretable prototype selection
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Alibi: Algorithms for monitoring and explaining machine learning models
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