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Algorithmic risk assessments are used to inform decisions in a wide variety of high-stakes settings.
42 U.S.C. § 2000e
Civil rights act, 1964 · 1964
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15 U.S.C. § 1691
Equal credit opportunity act, 1974 · 1974
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Varieties of selection bias
James J. Heckman · 1990
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Statistical modeling: The two cultures
Leo Breiman · 2001
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Does reject inference really improve the performance of application scoring models?
Jonathan Crook and John Banasik · 2004
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Fairness through awareness
Cynthia Dwork, Toniann Pitassi Moritz Hardt, Omer Reingold, and Richard Zemel · 2012
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A unifying view on dataset shift in classification
Jose G Moreno-Torres, Troy Raeder, Rocío Alaiz-Rodríguez, Nitesh V Chawla, and Francisco Herrera · 2012
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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A rule of thumb for reject inference in credit scoring
Guoping Zeng and Qi Zhao · 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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Certifying and removing disparate impact
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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Causal Inference for Statistics, Social and Biomedical Sciences: An Introduction
Guido W Imbens and Donald B Rubin · 2015
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Big data’s disparate impact
Solon Barocas and Andrew Selbst · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Reject inference in application scorecards: evidence from france
Ha-Thu Nguyen et al · 2016
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Socio-economic indexes for areas (seifa) technical paper
Australian Bureau of Statistics · 2016
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Machine bias. there’s software used across the country to predict future criminals. and it’s biased against blacks
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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Fairer and more accurate, but for whom?
Alexandra Chouldechova and Max G’Sell · 2017
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The selective labels problem: Evaluating algorithmic predictions in the presence of unobservables
Himabindu Lakkaraju, Jon Kleinberg, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan · 2017
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On fairness and calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger · 2017
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Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions
Alexandra Chouldechova, Diana Benavides-Prado, Oleksandr Fialko, and Rhema Vaithianathan · 2018
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Human decisions and machine predictions
Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan · 2018
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Amazon scraps secret ai recruiting tool that showed bias against women, Oct 2018
Jeffrey Dastin · 2018
Predictive multiplicity in classification
Charles T. Marx, Flavio du Pin Calmon, and Berk Ustun · 2019
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Fairness constraints: A flexible approach for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P. Gummadi · 2019
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Multiaccuracy: Black-box post-processing for fairness in classification
Michael P. Kim, Amirata Ghorbani, and James Zou · 2019
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Statistical analysis with missing data
Roderick JA Little and Donald B Rubin · 2019
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Fair transfer learning with missing protected attributes
Amanda Coston, Karthikeyan Natesan Ramamurthy, Dennis Wei, Kush R Varshney, Skyler Speakman, Zairah Mustahsan, and Supriyo Chakraborty · 2019
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Fairness and Machine Learning
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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach · 2018
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The cost of fairness in binary classification
Aditya Krishna Menon and Robert C Williamson · 2018
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Empirical risk minimization under fairness constraints
Michele Donini, Luca Oneto, Shai Ben-David, John R Shawe-Taylor, and Massimiliano A. Pontil · 2018
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Residual unfairness in fair machine learning from prejudiced data
Nathan Kallus and Angela Zhou · 2018
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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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Apple card investigated after gender discrimination complaints, Nov 2019
Neil Vigdor · 2019
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Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
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Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions
Shira Mitchell, Eric Potash, Solon Barocas, Alexander D’Amour, and Kristian Lum · 2019
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Two-player games for efficient non-convex constrained optimization
Andrew Cotter, Heinrich Jiang, and Karthik Sridharan · 2019
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Modeling risk and achieving algorithmic fairness using potential outcomes
Alan Mishler · 2019
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Predictably unequal? the effects of machine learning on credit markets
Andreas Fuster, Paul Goldsmith-Pinkham, Tarun Ramadorai, and Ansgar Walther · 2020
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Mitigating bias in algorithmic hiring: Evaluating claims and practices
Manish Raghavan, Solon Barocas, Jon Kleinberg, and Karen Levy · 2020
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Variable importance clouds: A way to explore variable importance for the set of good models
Jiayun Dong and Cynthia Rudin · 2020
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Lesia Semenova, Cynthia Rudin, and Ronald Parr · 2020
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Counterfactual risk assessments, evaluation and fairness
Amanda Coston, Alan Mishler, Edward H. Kennedy, and Alexandra Chouldechova · 2020
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Inferring the outcomes of rejected loans: An application of semisupervised clustering
Zhiyong Li, Xinyi Hu, Ke Li, Fanyin Zhou, and Feng Shen · 2020
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Deep generative models for reject inference in credit scoring
Rogelio A Mancisidor, Michael Kampffmeyer, Kjersti Aas, and Robert Jenssen · 2020
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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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Fairness violations and mitigation under covariate shift
Harvineet Singh, Rina Singh, Vishwali Mhasawade, and Rumi Chunara · 2021
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