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Artificial intelligence (AI) provides many opportunities to improve private and public life.
A VALUE FOR N-PERSON GAMES
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Deep content-based music recommendation
Probabilistic program abstractions
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Counterfactual fairness
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A unified approach to interpreting model predictions
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A. van den Oord, S. Dieleman, and B. Schrauwen · 2013
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Deep autoencoder neural networks for gene ontology annotation predictions
D. Chicco, P. Sadowski, and P. Baldi · 2014
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Interpreting tree ensembles with intrees
H. Deng · 2014
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A peek into the black box: exploring classifiers by randomization
A. Henelius, K. Puolamaki, H. Bostrom, L. Asker, and P. Papapetrou · 2014
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The bayesian case model: A generative approach for case-based reasoning and prototype classification
B. Kim, C. Rudin, and J. Shah · 2014
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
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Rule extraction from random forest: the rf+hc methods
M. Mashayekhi and R. Gras · 2015
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Explaining nonlinear classification decisions with deep taylor decomposition
G. Montavon, S. Lapuschkin, A. Binder, W. Samek, and K.-R. Müller · 2017
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Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
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Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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Distill-and-compare: Auditing black-box models using transparent model distillation
S. Tan, R. Caruana, G. Hooker, and Y. Lou · 2017
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Interpretable predictions of tree-based ensembles via actionable feature tweaking
G. Tolomei, F. Silvestri, A. Haines, and M. Lalmas · 2017
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Applications of bayesian network models in predicting types of hematological malignancies
R. Agrahari, A. Foroushani, T. R. Docking, L. Chang, G. Duns, M. Hudoba, A. Karsan, and H. Zare · 2018
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Theoretical impediments to machine learning with seven sparks from the causal revolution
J. Pearl · 2018
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Improving the explainability of random forest classifier - user centered approach
D. Petkovic, R. Altman, M. Wong, and A. Vigil · 2018
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Anchors: High-precision model-agnostic explanations, 2018
M. T. Ribeiro, S. Singh, and C. Guestrin · 2018
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
S. Wachter, B. Mittelstadt, and C. Russell · 2018
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A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins, et al · 2019
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Plan explanations as model reconciliation
T. Chakraborti, S. Sreedharan, S. Grover, and S. Kambhampati · 2019
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Explicable planning as minimizing distance from expected behavior
A. Kulkarni, Y. Zha, T. Chakraborti, S. G. Vadlamudi, Y. Zhang, and S. Kambhampati · 2019
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The explanation game: Explaining machine learning models with cooperative game theory, 2019
L. Merrick and A. Taly · 2019
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Zero-shot knowledge transfer via adversarial belief matching
P. Micaelli and A. J. Storkey · 2019
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Explanation in artificial intelligence: Insights from the social sciences
T. Miller · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
C. Rudin · 2019
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The challenge of crafting intelligible intelligence
D. S. Weld and G. Bansal · 2019
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Abstracting probabilistic models: A logical perspective
V. Belle · 2020
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Problems with shapley-value-based explanations as feature importance measures, 2020
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E. Kyrimi, S. Mossadegh, N. Tai, and W. Marsh · 2020
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