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Applications based on Machine Learning models have now become an indispensable part of the everyday life and the professional world.
An introduction to the bootstrap
B. Efron and R. J. Tibshirani · 1994
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Machine learning, 1997
T. M. Mitchell et al · 1997
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Asymptotic statistics
A. W. Van der Vaart · 1998
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Significance tests and confidence intervals for the adverse impact ratio
S. B. Morris and R. E. Lobsenz · 2000
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The origins of logistic regression
J. S. Cramer · 2002
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Field experiments of discrimination in the market place
P. A Riach and J. Rich · 2002
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Classification and regression trees, bagging, and boosting
C. D. Sutton · 2005
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Adverse impact and test validation: A practitioner’s guide to valid and defensible employment testing
D. Biddle · 2006
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The disparity between disparate treatment and disparate impact: An analysis of the ricci case
B.P. Winrow and C. Schieber · 2009
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Discrimination aware decision tree learning
F. Kamiran, T. Calders, and M. Pechenizkiy · 2010
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Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
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A study of top-k measures for discrimination discovery
D. Pedreschi, S. Ruggieri, and F. Turini · 2012
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A multidisciplinary survey on discrimination analysis
A. Romei and S. Ruggieri · 2014
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Credit scoring in Österreich, 07 2014
R. Rothmann, J. Krieger-Lamina, and W. Peissl · 2014
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Certifying and removing disparate impact
S. A Feldman, M.and Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian · 2015
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The black box society
F. Pasquale · 2015
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Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
J. Angwin, J. Larson, S. Mattu, and L. Kirchner · 2016
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Big data’s disparate impact
S. Barocas and A. D. Selbst · 2016
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Compas risk scales: Demonstrating accuracy equity and predictive parity
W. Dieterich, C. Mendoza, and T. Brennan · 2016
Cited alongside, same era.
Counterfactual fairness
M. J. Kusner, J. Loftus, C. Russell, and R. Silva · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
M B Zafar, I Valera, M Gomez Rodriguez, and K P Gummadi · 2017
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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 · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
J. Buolamwini and T. Gebru · 2018
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A comparative study of fairness-enhancing interventions in machine learning
S. A. Friedler, C. Scheidegger, S. Venkatasubramanian, S. Choudhary, E. P. Hamilton, and D. Roth · 2018
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Calibration for the (computationally-identifiable) masses
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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
A. W. Flores, K. Bechtel, and C.T. Lowenkamp · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
Cited alongside, same era.
Credit scoring in the era of big data
M. Hurley and J. Adebayo · 2016
Cited alongside, same era.
Inherent trade-offs in the fair determination of risk scores
J. Kleinberg, S. Mullainathan, and M. Raghavan · 2016
Cited alongside, same era.
Discrimination at Work
M. Mercat-Bruns · 2016
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
A. Chouldechova · 2017
Cited alongside, same era.
U. Hébert-Johnson, M. P. Kim, O. Reingold, and G. N. Rothblum · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
M. Kearns, S. Neel, A. Roth, and Z. S. Wu · 2018
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A central limit theorem for lp transportation cost on the real line with application to fairness assessment in machine learning
E. Del Barrio, P. Gordaliza, and J-M. Loubes · 2019
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Obtaining fairness using optimal transport theory
P. Gordaliza, E. Del Barrio, F. Gamboa, and J-M. Loubes · 2019
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ifair: Learning individually fair data representations for algorithmic decision making
P. Lahoti, K. P. Gummadi, and G. Weikum · 2019
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Fliptest: Fairness testing via optimal transport
E. Black, S. Yeom, and M. Fredrikson · 2020
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Mitigating bias in algorithmic hiring: Evaluating claims and practices
M. Raghavan, S. Barocas, J. Kleinberg, and K. Levy · 2020
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