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Fairness aware data mining (FADM) aims to prevent algorithms from discriminating against protected groups.
Selecting the strivers: a report on the preliminary results of the ets “educational strivers” study
A. P. Carnevale and E. Haghighat · 1998
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The black-white test score gap: An introduction
C. Jencks and M. Phillips · 1998
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Race-based suspect selection and colorblind equal protection doctrine and discourse
R. R. Banks · 2001
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Random forests
L. Breiman · 2001
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Justice as fairness: A restatement
J. Rawls · 2001
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Causation and race
P. W. Holland · 2003
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Measuring Racial Discrimination
R. M. Blank, M. Dabady, and C. F. Citro, editors · 2004
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It’s so hard to be fair
J. Brockner · 2006
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Discrimination-aware data mining
D. Pedreschi, S. Ruggieri, and F. Turini · 2008
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Causality: Models, Reasoning and Inference
J. Pearl · 2009
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Three naive bayes approaches for discrimination-free classification
T. Calders and S. Verwer · 2010
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Discrimination aware decision tree learning
F. Kamiran, T. Calders, and M. Pechenizkiy · 2010
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Implementing anti-discrimination policies in statistical profiling models
D. G. Pope and J. R. Sydnor · 2011
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Fairness-aware classifier with prejudice remover regularizer
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2012
Cited alongside, same era.
Controlling attribute effect in linear regression
T. Calders, A. Karim, F. Kamiran, W. Ali, and X. Zhang · 2013
Cited alongside, same era.
A methodology for direct and indirect discrimination prevention in data mining
S. Hajian and J. Domingo-Ferrer · 2013
Cited alongside, same era.
Quantifying explainable discrimination and removing illegal discrimination in automated decision making
F. Kamiran, I. Zliobaite, and T. Calders · 2013
Cited alongside, same era.
John rawls
L. Wenar · 2013
Cited alongside, same era.
R. S. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
Counterfactual fairness
M. J. Kusner, J. Loftus, C. Russell, and R. Silva · 2017
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Auditing black-box models for indirect influence
P. Adler, C. Falk, S. A. Friedler, T. Nix, G. Rybeck, C. Scheidegger, B. Smith, and S. Venkatasubramanian · 2018
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A causal bayesian networks viewpoint on fairness
S. Chiappa and W. S. Isaac · 2018
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Automating inequality: How high-tech tools profile, police, and punish the poor
V. Eubanks · 2018
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Fairness behind a veil of ignorance: A welfare analysis for automated decision making
H. Heidari, C. Ferrari, K. Gummadi, and A. Krause · 2018
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Algorithms of oppression
S. U. Noble · 2018
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Counterfactuals and Causal Inference: Methods and Principles for Social Research
S. L. Morgan and C. Winship · 2014
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Equality of opportunity
R. Arneson · 2015
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Support Vector Regression
M. Awad and R. Khanna · 2015
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The case for process fairness in learning: Feature selection for fair decision making
N. Grgic-Hlaca, M. B. Zafar, K. P. Gummadi, and A. Weller · 2016
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Credit scoring in the era of big data
M. Hurley and J. Adebayo · 2016
Cited alongside, same era.
Norm-referenced tests and race-blind admissions: the case for eliminating the sat and act at the university of california
S. Geiser · 2017
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S. Benthall and B. D. Haynes · 2019
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A moral framework for understanding fair ml through economic models of equality of opportunity
H. Heidari, M. Loi, K. P. Gummadi, and A. Krause · 2019
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R: A Language and Environment for Statistical Computing
R Core Team · 2019
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P. Ravishankar, P. Malviya, and B. Ravindran · 2020
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Fairness in criminal justice risk assessments: The state of the art
R. Berk, H. Heidari, S. Jabbari, M. Kearns, and A. Roth · 2021
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Fairness in credit scoring: Assessment, implementation and profit implications
N. Kozodoi, J. Jacob, and S. Lessmann · 2021
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The input fallacy
T. B. Gillis · 2022
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