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A recent flurry of research activity has attempted to quantitatively define "fairness" for decisions based on statistical and machine learning (ML) predictions.
A framework for understanding unintended consequences of machine learning
Harini Suresh and John V Guttag · 1901
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The theory of decision procedures for distributions with monotone likelihood ratio
Samuel Karlin and Herman Rubin · 1956
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Test bias: Validity of the scholastic aptitude test for negro and white students in integrated colleges
Anne T. Cleary · 1966
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Another look at ‘cultural fairness’
Richard B. Darlington · 1971
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Methodological considerations relevant to discrimination in employment testing
Hillel J. Einhorn and Alan R. Bass · 1971
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A Theory of Justice
John Rawls · 1971
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Concepts of culture-fairness
Robert L. Thorndike · 1971
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Critical analysis of the statistical and ethical implications of various definitions of test bias
John E Hunter and Frank L Schmidt · 1976
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An evaluation of some models for culture-fair selection
Nancy S. Petersen and Melvin R. Novick · 1976
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Inference and missing data
Donald B Rubin · 1976
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A comparison of three models for determining test fairness
Mary A. Lewis · 1978
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Statistical Decision Theory and Bayesian Analysis
James O. Berger · 1985
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Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics
Kimberle Crenshaw · 1989
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Lessons learned: Statistical techniques and fair lending
Marsha Courchane, David Nebhut, and David Nickerson · 2000
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Credit, capital and community: Informal banking in immigrant communities in the united states, 1880–1924
Jared N Day · 2002
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Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care
Institutes of Medicine (IOM) · 2003
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To model or not to model? competing modes of inference for finite population sampling
Roderick J Little · 2004
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Pretrial services programs: Responsibilities and potential
Barry Mahoney, Bruce D Beaudin, John A Carver III, Daniel B Ryan, and Richard B Hoffman · 2004
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Causal inference using potential outcomes: Design, modeling, decisions
Donald B. Rubin · 2005
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Against prediction: Profiling, policing, and punishing in an actuarial age
Bernard E. Harcourt · 2008
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Discrimination-aware data mining
Dino Pedreshi, Salvatore Ruggieri, and Franco Turini · 2008
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Classifying without discriminating
Faisal Kamiran and Toon Calders · 2009
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Classifying without discriminating
Faisal Kamiran and Toon Calders · 2009
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Causality
Judea Pearl · 2009
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Three naive bayes approaches for discrimination-free classification
Toon Calders and Sicco Verwer · 2010
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Discrimination aware decision tree learning
Faisal Kamiran, Toon Calders, and Mykola Pechenizkiy · 2010
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All of Statistics: A Concise Course in Statistical Inference
Larry Wasserman · 2010
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Causal effects of perceived immutable characteristics
James D. Greiner and Donald B. Rubin · 2011
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Fairness-aware learning through regularization approach
Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma · 2011
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State of the science of pretrial release recommendations and supervision
Marie VanNostrand, Kenneth Rose, and Kimberly Weibrecht · 2011
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The New Jim Crow
Michelle Alexander · 2012
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Results on differential and dependent measurement error of the exposure and the outcome using signed directed acyclic graphs
Tyler J. VanderWeele and Miguel A. Hernán · 2012
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Bayesian Data Analysis, Third Edition
Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin · 2013
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Discrimination in online ad delivery
Latanya Sweeney · 2013
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Children in the public benefit system at risk of maltreatment: Identification via predictive modeling
Rhema Vaithianathan, Tim Maloney, Emily Putnam-Hornstein, and Nan Jiang · 2013
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Causal diagrams for interference
Elizabeth L Ogburn, Tyler J VanderWeele, et al · 2014
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On causal interpretation of race in regressions adjusting for confounding and mediating variables
Tyler J. VanderWeele and Whitney R. Robinson · 2014
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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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The connection between varying treatment effects and the crisis of unreplicable research: A bayesian perspective, 2015
Andrew Gelman · 2015
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Causal Inference in Statistics, Social, and Biomedical Sciences
Guido W. Imbens and Donald B. Rubin · 2015
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Can an algorithm hire better than a human?
Claire Cain Miller · 2015
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When algorithms discriminate
Claire Cain Miller · 2015
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A complete generalized adjustment criterion
Emilija Perković, Johannes Textor, Markus Kalisch, and Marloes H Maathuis · 2015
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Predictive modeling for public health: Preventing childhood lead poisoning
Eric Potash, Joe Brew, Alexander Loewi, Subhabrata Majumdar, Andrew Reece, Joe Walsh, Eric Rozier, Emile Jorgenson, Raed Mansour, and Rayid Ghani · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Big data’s disparate impact
Solon Barocas and Andrew D. Selbst · 2016
Cited alongside, same era.
Compas risk scales: Demonstrating accuracy equity and predictive parity
William Dieterich, Christina Mendoza, and Tim Brennan · 2016
Cited alongside, same era.
Compas risk scales: Demonstrating accuracy equity and predictive parity
William Dieterich, Christina Mendoza, and Tim Brennan · 2016
Cited alongside, same era.
Fairness in Educational Assessment and Measurement
Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
Virginia Eubanks · 2018
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Aequitas
Center for Data Science and University of Chicago Public Policy · 2018
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A comparative study of fairness-enhancing interventions in machine learning
Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P. Hamilton, and Derek Roth · 2018
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The algorithm that could save vulnerable new yorkers from being forced out of their homes
Sidney Fussell · 2018
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Predictably unequal? the effects of machine learning on credit markets
Andreas Fuster, Paul Goldsmith-Pinkham, Tarun Ramadorai, and Ansgar Walther · 2018
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Neil J. Dorans and Linda L. Cook · 2016
Cited alongside, same era.
Big data: A report on algorithmic systems, opportunity, and civil rights
Executive Office of the President and Cecilia Muñoz (Director, Domestic Policy Council) and Megan Smith (U.S. Chief Technology Officer, Office of Science and Technology Policy) and DJ Patil (Deputy Chief Technology Officer for Data Policy and Chief Data Scientist, Office of Science and Technology Policy) · 2016
Cited alongside, same era.
False positives, false negatives, and false analyses: A rejoinder to machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
Anthony W. Flores, Kristin Bechtel, and Christopher T. Lowenkamp · 2016
Cited alongside, same era.
On the (im)possibility of fairness
Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
Cited alongside, same era.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
Cited alongside, same era.
Immigration detention in the risk classification assessment era
Robert Koulish · 2016
Cited alongside, same era.
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumeé III, and Kate Crawford · 2018
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“fair” risk assessments: A precarious approach for criminal justice reform
Ben Green · 2018
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The myth in the methodology: Towards a recontextualization of fairness in machine learning
Ben Green and Lily Hu · 2018
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Causal Inference
Miguel A. Hernán and James M. Robins · 2018
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The dataset nutrition label: A framework to drive higher data quality standards
Sarah Holland, Ahmed Hosny, Sarah Newman, Joshua Joseph, and Kasia Chmielinski · 2018
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A short-term intervention for long-term fairness in the labor market
Lily Hu and Yiling Chen · 2018
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Welfare and distributional impacts of fair classification
Lily Hu and Yiling Chen · 2018
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Decomposition analysis to identify intervention targets for reducing disparities
John W Jackson and Tyler J VanderWeele · 2018
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Residual unfairness in fair machine learning from prejudiced data
Nathan Kallus and Angela Zhou · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
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Fairness through computationally-bounded awareness
Michael P. Kim, Omer Reingold, and Guy N. Rothblum · 2018
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Algorithmic fairness
Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Ashesh Rambachan · 2018
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Danger ahead: Risk assessment and the future of bail reform
John Logan Koepke and David G Robinson · 2018
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Eddie murphy and the dangers of counterfactual causal thinking about detecting racial discrimination
Issa Kohler-Hausmann · 2018
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Does mitigating ml’s impact disparity require treatment disparity?
Zachary Lipton, Julian McAuley, and Alexandra Chouldechova · 2018
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Delayed impact of fair machine learning
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt · 2018
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru · 2018
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Fair inference on outcomes
Razieh Nabi and Ilya Shpitser · 2018
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21 fairness definitions and their politics, 2018
Arvind Narayanan · 2018
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Beyond legitimation: Rethinking fairness, interpretability, and accuracy in machine learning
Rodrigo Ochigame, Chelsea Barabas, Karthik Dinakar, Madars Virza, and Joichi Ito · 2018
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Legionnaires’ disease response: Moda assisted in a citywide response effort after an outbreak of legionnaires’ disease
The Mayor’s Office of Data Analytics · 2018
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Near term artificial intelligence and the ethical matrix, 2018
Cathy O’Neil and Hanna Gunn · 2018
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Failing loudly: An empirical study of methods for detecting dataset shift
Stephan Rabanser, Stephan Günnemann, and Zachary C. Lipton · 2018
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Criminalizing homelessness violates basic human rights
John Raphling · 2018
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Probably approximately metric-fair learning
Guy N. Rothblum and Gal Yona · 2018
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Please stop explaining black box models for high stakes decisions
Cynthia Rudin · 2018
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Algorithms, platforms, and ethnic bias: An integrative essay
Selena Silva and Martin Kenney · 2018
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Refining the concept of a nutritional label for data and models
Julia Stoyanovich · 2018
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Fairness definitions explained
Sahil Verma and Julia Rubin · 2018
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A nutritional label for rankings
Ke Yang, Julia Stoyanovich, Abolfazl Asudeh, Bill Howe, HV Jagadish, and Gerome Miklau · 2018
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Using stacking to average bayesian predictive distributions
Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman, et al · 2018
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Fairness in decision-making–the causal explanation formula
Junzhe Zhang and Elias Bareinboim · 2018
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The what-if tool: Code-free probing of machine learning models
Google · 2019
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50 years of test (un)fairness: Lessons for machine learning
Ben Hutchinson and Margaret Mitchell · 2019
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Eliciting and enforcing subjective individual fairness, 2019
Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, and Zhiwei Steven Wu · 2019
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Problem formulation and fairness
Samir Passi and Solon Barocas · 2019
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Fairness and abstraction in sociotechnical systems
Andrew D. Selbst, danah boyd, Sorelle Friedler, Suresh Venkatasubramanian, and Janet Vertesi · 2019
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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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