Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
Later among the works it cites.
Problems in using p-curve analysis and text-mining to detect rate of p-hacking and evidential value
Dorothy V. M. Bishop, Jun Chen, and Paul A. Thompson · 2016
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Evaluating replicability of laboratory experiments in economics
Colin F. Camerer, Anna Dreber, Eskil Forsell, Teck-Hua Ho, Jürgen Huber, Magnus Johannesson, Michael Kirchler, Johan Almenberg, Adam Altmejd, Taizan Chan, Emma Heikensten, Felix Holzmeister, Taisuke Imai, Siri Isaksson, Gideon Nave, Thomas Pfeiffer, Michael Razen, and Hang Wu · 2016
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COMPAS risk scales: Demonstrating accuracy equity and predictive parity
William Dieterich, Christina Mendoza, and Tim Brennan · 2016
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Sensitivity analysis without assumptions
Peng Ding and Tyler J. VanderWeele · 2016
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Comment on “estimating the reproducibility of psychological science”
Daniel T. Gilbert, Gary King, Stephen Pettigrew, and Timothy D. Wilson · 2016
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Generalized fiducial inference: A review and new results
Jan Hannig, Hari Iyer, Randy C.S. Lai, and Thomas C. M. Lee · 2016
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Prediction uncertainty and optimal experimental design for learning dynamical systems
Benjamin Letham, Portia A. Letham, Cynthia Rudin, and Edward Browne · 2016
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Exact post-selection inference for sequential regression procedures
Ryan J. Tibshirani, Jonathan Taylor, Richard Lockhart, and Robert Tibshirani · 2016
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The ASA’s statement on p-values: Context, process, and purpose
Ronald L. Wasserstein and Nicole A. Lazar · 2016
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Degrees of freedom in planning, running, analyzing, and reporting psychological studies: A checklist to avoid p-hacking
Jelte M. Wicherts, Coosje L.S. Veldkamp, Hilde E.M. Augusteijn, Marjan Bakker, Robbie C.M. van Aert, and Marcel A.L.M. van Assen · 2016
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Pretrial detention reform — recommendations to the Chief Justice
Brian J. Back, Lisa R. Rodriguez, Mark Boessenecker, Alex Calvo, Arturo Castro, Hillary A. Chittick, George C. Eskin, Scott M. Gordon, Teri L. Jackson, Brian L. McCabe, Serena R. Murillo, and Rise Jones Pichon · 2017
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Fairness in criminal justice risk assessments: The state of the art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth · 2017
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Handbook of uncertainty quantification
Roger Ghanem, David Higdon, and Houman Owhadi · 2017
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Surrogate models for uncertainty quantification: An overview
Bruno Sudret, Stefano Marelli, and Joe Wiart · 2017
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Metric learning for kernel regression
Kilian Q. Weinberger and Gerald Tesauro · 2017
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Model uncertainty and robustness a computational framework for multimodel analysis
Cristobal Young and Katherine Holsteen · 2017
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Interpretable classification models for recidivism prediction
Jiaming Zeng, Berk Ustun, and Cynthia Rudin · 2017
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Learning certifiably optimal rule lists for categorical data
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, and Cynthia Rudin · 2018
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Redefine statistical significance
Daniel J. Benjamin, James O. Berger, Magnus Johannesson, Brian A. Nosek, E. J. Wagenmakers, Richard Berk, Kenneth A. Bollen, Björn Brembs, Lawrence Brown, Colin Camerer, David Cesarini, Christopher D. Chambers, Merlise Clyde, Thomas D. Cook, Paul De Boeck, Zoltan Dienes, Anna Dreber, Kenny Easwaran, Charles Efferson, Ernst Fehr, Fiona Fidler, Andy P. Field, Malcolm Forster, Edward I. George, Richard Gonzalez, Steven Goodman, Edwin Green, Donald P. Green, Anthony G. Greenwald, Jarrod D. Hadfield, Larry V. Hedges, Leonhard Held, Teck Hua Ho, Herbert Hoijtink, Daniel J. Hruschka, Kosuke Imai, Guido Imbens, John P. A. Ioannidis, Minjeong Jeon, James Holland Jones, Michael Kirchler, David Laibson, John List, Roderick Little, Arthur Lupia, Edouard Machery, Scott E. Maxwell, Michael McCarthy, Don A. Moore, Stephen L. Morgan, Marcus Munafó, Shinichi Nakagawa, Brendan Nyhan, Timothy H. Parker, Luis Pericchi, Marco Perugini, Jeff Rouder, Judith Rousseau, Victoria Savalei, Felix D. Schönbrodt, Thomas Sellke, Betsy Sinclair, Dustin Tingley, Trisha Van Zandt, Simine Vazire, Duncan J. Watts, Christopher Winship, Robert L. Wolpert, Yu Xie, Cristobal Young, Jonathan Zinman, and Valen E. Johnson · 2018
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Hypothesis tests that are robust to choice of matching method
Original
Marco Morucci, Md Noor-E-Alam, and Cynthia Rudin · 2018
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We ran 9 billion regressions: Eliminating false positives through computational model robustness
John Muñoz and Cristobal Young · 2018
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All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously
Aaron Fisher, Cynthia Rudin, and Francesca Dominici · 2019
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The age of secrecy and unfairness in recidivism prediction
Cynthia Rudin, Caroline Wang, and Beau Coker · 2020
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