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As machine learning is increasingly used to help make decisions, there is a demand for these decisions to be explainable.
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Regulation (EU) 2016/679 of the European Parliament and of the Council
EU Data Protection Regulation · 2016
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Interpretable decision sets: A joint framework for description and prediction
H. Lakkaraju, S. H. Bach, and J. Leskovec · 2016
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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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F. Doshi-Velez and B. Kim · 2017
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B. Goodman and S. R. Flaxman · 2017
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A unified approach to interpreting model predictions
S. M. Lundberg and S. Lee · 2017
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A. Shih, A. Choi, and A. Darwiche · 2018
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Artificial Intelligence Roadmap
Australian Government · 2019
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IMLI: an incremental framework for maxsat-based learning of interpretable classification rules
B. Ghosh and K. S. Meel · 2019
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Factual and counterfactual explanations for black box decision making
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D. Gunning and D. Aha · 2019
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RC2: an efficient maxsat solver
A. Ignatiev, A. Morgado, and J. Marques-Silva · 2019
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A. Ignatiev, N. Narodytska, and J. Marques-Silva · 2019
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On relating explanations and adversarial examples
A. Ignatiev, N. Narodytska, and J. Marques-Silva · 2019
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Three modern roles for logic in AI
A. Darwiche · 2020
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Towards trustable explainable AI
A. Ignatiev · 2020
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