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Explainability in AI is crucial for model development, compliance with regulation, and providing operational nuance to predictions.
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Explanations can be manipulated and geometry is to blame
A.-K. Dombrowski, M. Alber, C. Anders, M. Ackermann, K.-R. Müller, and P. Kessel · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
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Why should I trust you: Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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A universal marginalizer for amortized inference in generative models
L. Douglas, I. Zarov, K. Gourgoulias, C. Lucas, C. Hart, A. Baker, M. Sahani, Y. Perov, and S. Johri · 2017
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S. M. Lundberg and S.-I. Lee · 2017
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Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
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C. Rudin · 2019
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The many Shapley values for model explanation
M. Sundararajan and A. Najmi · 2019
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Fairwashing explanations with off-manifold detergent
C. J. Anders, P. Pasliev, A.-K. Dombrowski, K.-R. Müller, and P. Kessel · 2020
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You shouldn’t trust me: Learning models which conceal unfairness from multiple explanation methods
B. Dimanov, U. Bhatt, M. Jamnik, and A. Weller · 2020
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From local explanations to global understanding with explainable AI for trees
S. M. Lundberg, G. Erion, H. Chen, A. DeGrave, J. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S. Lee · 2020
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Fooling lime and shap: Adversarial attacks on post hoc explanation methods
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