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Where machine-learned predictive risk scores inform high-stakes decisions, such as bail and sentencing in criminal justice, fairness has been a serious concern.
Verification of forecasts expressed in terms of probability
G. W. Brier · 1950
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The meaning and use of the area under a receiver operating characteristic (roc) curve
J. Hanley and B. McNeil · 1982
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Coronary risk prediction in adults (the framingham heart study)
P. W. Wilson, W. P. Castelli, and W. B. Kannel · 1987
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Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach
E. DeLong, D. DeLong, and D. L. Clarke-Pearson · 1988
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Auc optimization vs. error rate minimization
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Learnability of bipartite ranking functions
S. Agarwal and D. Roth · 2005
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Translating clinical research into clinical practice: Impact of using prediction rules to make decisions
B. Reilly and A. Evans · 2006
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Risk-need-responsivity model for offender assessment and rehabilitation
J. Bonta and D. Andrews · 2007
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Measuring classifier performance: a coherent alternative to the area under the roc curve
D. Hand · 2009
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A process for predicting manhole events in manhattan
C. Rudin, R. J. Passonneau, A. Radeva, H. Dutta, SteveIerome, and D. Isaac · 2010
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Meaningful use in practice: Using patient-specific risk in an electronic health record for shared decision making
J. Jones, N. Shah, C. Bruce, and W. F. Stewart · 2011
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Foundations of Machine Learning
M. Mohri, A. Rostamizadeh, and A. Talwalkar · 2012
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On the relationship between binary classification, bipartite ranking, and binary class probability estimation
H. Narasimhan and S. Agarwal · 2013
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Big data’s disparate impact
S. Barocas and A. Selbst · 2014
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Certifying and removing disparate impact
J. M. C. S. S. V. Michael Feldman, Sorelle Friedler · 2015
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Machine bias
J. Angwin, J. Larson, S. Mattu, and L. Kirchner · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
A. Chouldechova · 2016
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Compas risk scales: Demonstrating accuracy equity and predictive parity
W. Dieterich, C. Mendoza, and T. Brennan · 2016
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Equality of opportunity in supervised learning
M. Hardt, E. Price, N. Srebro, et al · 2016
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Ranking with fairness constraints
L. E. Celis, D. Straszak, and N. K. Vishnoi · 2018
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Use of predictive risk scores for early admission to the icu
C. Chan, G. Escobar, and J. Zubizarreta · 2018
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Why is my classifier discriminatory?
I. Chen, F. Johansson, and D. Sontag · 2018
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A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions
A. Chouldechova, E. Putnam-Hornstein, D. Benavides-Prado, O. Fialko, and R. Vaithianathan · 2018
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The measure and mismeasure of fairness: A critical review of fair machine learning
S. Corbett-Davies and S. Goel · 2018
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Predictably unequal? the effects of machine learning on credit markets
A. Fuster, P. Goldsmith-Pinkham, T. Ramadorai, and A. Walther · 2018
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Measuring fairness in ranked outputs
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Multicalibration: Calibration for the (computationally-identifiable) masses
U. Hebert-Johnson, M. Kim, O. Reingold, and G. Rothblum · 2018
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Who calls for help? statistical evidence of disparities in citizen-government interactions using geo-spatial survey and 311 data from kansas city
C. E. Kontokosta and B. Hong · 2018
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Group calibration is a byproduct of unconstrained learning
L. Liu, M. Simchowitz, and M. Hardt · 2018
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Ensuring fairness in machine learning to advance health equity
A. Rajkomar, M. Hardt, M. D. Howell, G. Corrado, and M. H. Chin · 2018
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Fairness of exposure in rankings
A. Singh and T. Joachims · 2018
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A comparative study of fairness-enhancing interventions in machine learning
S. Friedler, C. Scheidegger, S. Venkatasubramanian, S. Choudhary, E. P. Hamilton, and D. Roth · 2019
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Improving fairness in machine learning systems: What do industry practitioners need?
K. Holstein, J. W. Vaughan, H. D. III, M. Dudík, and H. Wallach · 2019
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