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The ability to interpret machine learning models has become increasingly important now that machine learning is used to inform consequential decisions.
Neuronlike adaptive elements that can solve difficult learning control problems
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Using data mining to predict secondary school student performance
Paulo Cortez and Alice Maria Gonçalves Silva · 2008
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Discrimination-aware data mining
Dino Pedreshi, Salvatore Ruggieri, and Franco Turini · 2008
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Causality
Judea Pearl · 2009
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Collision avoidance for unmanned aircraft using markov decision processes
Selim Temizer, Mykel Kochenderfer, Leslie Kaelbling, Tomas Lozano-Pérez, and James Kuchar · 2010
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Intelligible models for classification and regression
Rich Caruana, Yin Lou, and Johannes Gehrke · 2012
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 2014
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Interpreting tree ensembles with intrees
Houtao Deng · 2014
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Algorithms for interpretable machine learning
Cynthia Rudin · 2014
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad · 2015
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Algorithm aversion: People erroneously avoid algorithms after seeing them err
Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey · 2015
Supersparse linear integer models for optimized medical scoring systems
Berk Ustun and Cynthia Rudin · 2016
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Mediboost: a patient stratification tool for interpretable decision making in the era of precision medicine
Gilmer Valdes, José Marcio Luna, Eric Eaton, Charles B Simone, et al · 2016
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Genesim: genetic extraction of a single, interpretable model
Gilles Vandewiele, Olivier Janssens, Femke Ongenae, Filip De Turck, and Sofie Van Hoecke · 2016
Later among the works it cites.
https://gym.openai.com/envs/CartPole-v0
Openai cartpole-v0 environment · 2017
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http://archive.ics.uci.edu/ml
Uci machine learning repository · 2017
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Learning certifiably optimal rule lists for categorical data
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Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
Benjamin Letham, Cynthia Rudin, Tyler H McCormick, David Madigan, et al · 2015
Cited alongside, same era.
Falling rule lists
Fulton Wang and Cynthia Rudin · 2015
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
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Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, and Cynthia Rudin · 2017
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A roadmap for a rigorous science of interpretability
Finale Doshi-Velez and Been Kim · 2017
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Simple rules for complex decisions
Jongbin Jung, Connor Concannon, Ravi Shroff, Sharad Goel, and Daniel G Goldstein · 2017
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Human decisions and machine predictions
Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan · 2017
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Understanding black-box predictions via influence functions
P. W. Koh and P. Liang · 2017
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