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Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings.
Pattern Recognition and Machine Learning
C. M. Bishop · 2006
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Discrimination-aware Data Mining
D. Pedreschi, S. Ruggieri, and F. Turini · 2008
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Doubly Robust Internal Benchmarking and False Discovery Rates for Detecting Racial Bias in Police Stops
J. M. Greg Ridgeway · 2009
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Classification with No Discrimination by Preferential Sampling
F. Kamiran and T. Calders · 2010
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Fairness-aware Classifier with Prejudice Remover Regularizer
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2011
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kNN as an Implementation of Situation Testing for Discrimination Discovery and Prevention
B. T. Luong, S. Ruggieri, and F. Turini · 2011
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Fairness Through Awareness
C. Dwork, M. Hardt, T. Pitassi, and O. Reingold · 2012
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Discrimination in Online Ad Delivery
L. Sweeney · 2013
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R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
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Big Data: Seizing Opportunities, Preserving Values
J. Podesta, P. Pritzker, E. Moniz, J. Holdren, and J. Zients · 2014
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A Multidisciplinary Survey on Discrimination Analysis
A. Romei and S. Ruggieri · 2014
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Certifying and Removing Disparate Impact
M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian · 2015
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Precinct or Prejudice? Understanding Racial Disparities in New York City’s Stop-and-Frisk Policy
S. Goel, J. M. Rao, and R. Shroff · 2015
Cited alongside, same era.
https://www.documentcloud.org/documents/2702103-Sample-Risk-Assessment-COMPAS-CORE.html
2016
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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
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, C. T. Lowenkamp, and K. Bechtel · 2016
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Satisfying Real-world Goals with Dataset Constraints
G. Goh, A. Cotter, M. Gupta, and M. Friedlander · 2016
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Equality of Opportunity in Supervised Learning
M. Hardt, E. Price, and N. Srebro · 2016
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https://github.com/propublica/compas-analysis
J. Larson, S. Mattu, L. Kirchner, and J. Angwin · 2016
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How We Analyzed the COMPAS Recidivism Algorithm
J. Larson, S. Mattu, L. Kirchner, and J. Angwin · 2016
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Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights
C. Muñoz, M. Smith, and D. Patil · 2016
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Big Data’s Disparate Impact
S. Barocas and A. D. Selbst · 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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Artificial Intelligence’s White Guy Problem
K. Crawford · 2016
Cited alongside, same era.
https://en.wikipedia.org/wiki/Stop-and-frisk\_in\_New\_York\_City
Stop-and-frisk in New York City
Cited in the paper.
Bias in Criminal Risk Scores Is Mathematically Inevitable, Researchers Say
J. Angwin and J. Larson
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Disciplined Convex-Concave Programming
X. Shen, S. Diamond, Y. Gu, and S. Boyd · 2016
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Inherent Trade-Offs in the Fair Determination of Risk Scores
J. Kleinberg, S. Mullainathan, and M. Raghavan · 2017
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Fairness Constraints: Mechanisms for Fair Classification
M. B. Zafar, I. V. Martinez, M. G. Rodriguez, and K. P. Gummadi · 2017
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