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As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs.
Studies in the logic of explanation
Carl Hempel and Paul Oppenheim · 1948
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{ \{ UCI } \} repository of machine learning databases
Catherine Blake and Christopher J Merz · 1998
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Rethinking mechanistic explanation
Stuart Glennan · 2002
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Complexity measures of supervised classification problems
Tin Kam Ho and Mitra Basu · 2002
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Human Memory
Ian Neath and Aimee Surprenant · 2003
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What lies beneath? understanding the limits of understanding
Frank Keil, Leonid Rozenblit, and Candice Mills · 2004
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Explanation: A mechanist alternative
William Bechtel and Adele Abrahamsen · 2005
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Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Speculations on human causal learning and reasoning
Nick Chater and Mike Oaksford · 2006
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Explanation and understanding
Frank Keil · 2006
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The structure and function of explanations
Tania Lombrozo · 2006
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Privacy versus antidiscrimination
Lior Jacob Strahilevitz · 2008
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Reading tea leaves: How humans interpret topic models
Jonathan Chang, Jordan L Boyd-Graber, Sean Gerrish, Chong Wang, and David M Blei · 2009
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On the geometry of differential privacy
Moritz Hardt and Kunal Talwar · 2010
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Research methods in human-computer interaction
Jonathan Lazar, Jinjuan Heidi Feng, and Harry Hochheiser · 2010
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Data mining for discrimination discovery
Salvatore Ruggieri, Dino Pedreschi, and Franco Turini · 2010
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Adnostic: Privacy preserving targeted advertising
Vincent Toubiana, Arvind Narayanan, Dan Boneh, Helen Nissenbaum, and Solon Barocas · 2010
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Structuring dimensions for collaborative systems evaluation
Pedro Antunes, Valeria Herskovic, Sergio F Ochoa, and Jose A Pino · 2012
Cited alongside, same era.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Cited alongside, same era.
Intelligible models for classification and regression
Yin Lou, Rich Caruana, and Johannes Gehrke · 2012
Cited alongside, same era.
Inferring robot task plans from human team meetings: A generative modeling approach with logic-based prior
Been Kim, Caleb Chacha, and Julie Shah · 2013
Cited alongside, same era.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Cited alongside, same era.
Safe and interpretable machine learning: A methodological review
Identifying police officers at risk of adverse events
Samuel Carton, Jennifer Helsby, Kenneth Joseph, Ayesha Mahmud, Youngsoo Park, Joe Walsh, Crystal Cody, CPT Estella Patterson, Lauren Haynes, and Rayid Ghani · 2016
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Meta-unsupervised-learning: A supervised approach to unsupervised learning
Vikas K Garg and Adam Tauman Kalai · 2016
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European union regulations on algorithmic decision-making and a” right to explanation”
Bryce Goodman and Seth Flaxman · 2016
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Monotonic calibrated interpolated look-up tables
Maya Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Canini, Alexander Mangylov, Wojciech Moczydlowski, and Alexander Van Esbroeck · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Clemens Otte · 2013
Cited alongside, same era.
The ethics of artificial intelligence
Nick Bostrom and Eliezer Yudkowsky · 2014
Cited alongside, same era.
Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis
Finale Doshi-Velez, Yaorong Ge, and Isaac Kohane · 2014
Cited alongside, same era.
Comprehensible classification models: a position paper
Alex Freitas · 2014
Cited alongside, same era.
Openml: networked science in machine learning
Joaquin Vanschoren, Jan N Van Rijn, Bernd Bischl, and Luis Torgo · 2014
Cited alongside, same era.
Graph-sparse lda: a topic model with structured sparsity
Finale Doshi-Velez, Byron Wallace, and Ryan Adams · 2015
Cited alongside, same era.
Hidden technical debt in machine learning systems
D Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, and Dan Dennison · 2015
Cited alongside, same era.
Interpretable decision sets: A joint framework for description and prediction
Himabindu Lakkaraju, Stephen H Bach, and Jure Leskovec · 2016
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola · 2016
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General data protection regulation
Parliament and Council of the European Union · 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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Compositional inductive biases in function learning
Eric Schulz, Joshua Tenenbaum, David Duvenaud, Maarten Speekenbrink, and Samuel Gershman · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Automatic neural reconstruction from petavoxel of electron microscopy data
Adi Suissa-Peleg, Daniel Haehn, Seymour Knowles-Barley, Verena Kaynig, Thouis R Jones, Alyssa Wilson, Richard Schalek, Jeffery W Lichtman, and Hanspeter Pfister · 2016
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On the safety of machine learning: Cyber-physical systems, decision sciences, and data products
Kush Varshney and Homa Alemzadeh · 2016
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Axis: Generating explanations at scale with learnersourcing and machine learning
Joseph Jay Williams, Juho Kim, Anna Rafferty, Samuel Maldonado, Krzysztof Z Gajos, Walter S Lasecki, and Neil Heffernan · 2016
Later among the works it cites.
CMU computer won poker battle over humans by statistically significant margin
Sean Hamill · 2017
Closest in time.
Bayesian rule sets for interpretable classification
Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, and Perry MacNeille · 2017
Closest in time.