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Risk assessment tools are widely used around the country to inform decision making within the criminal justice system.
Inference and missing data
Donald B Rubin · 1976
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Sample selection bias as a specification error
James J Heckman · 1979
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Correlates of delinquency: The illusion of discrepancy between self-report and official measures
Michael J Hindelang, Travis Hirschi, and Joseph G Weis · 1979
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Theoretical statistics
David Victor Hinkley and DR Cox · 1979
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Reassessing the reliability and validity of self-report delinquency measures
David Huizinga and Delbert S Elliott · 1986
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Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models
James M Robins, Andrea Rotnitzky, and Daniel O Scharfstein · 2000
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Convex optimization
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Raymond J Carroll, David Ruppert, Ciprian M Crainiceanu, and Leonard A Stefanski · 2006
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Ranking and empirical minimization of u-statistics
Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis, et al · 2008
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Learning classifiers from only positive and unlabeled data
Charles Elkan and Keith Noto · 2008
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Assessing the race–crime and ethnicity–crime relationship in a sample of serious adolescent delinquents
Alex R Piquero and Robert W Brame · 2008
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Novelty detection: Unlabeled data definitely help
Clayton Scott and Gilles Blanchard · 2009
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Causal inference with differential measurement error: Nonparametric identification and sensitivity analysis
Kosuke Imai and Teppei Yamamoto · 2010
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Risk assessment instruments validated and implemented in correctional settings in the united states
Sarah Desmarais and Jay Singh · 2013
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Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
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Classification with asymmetric label noise: Consistency and maximal denoising
Clayton Scott, Gilles Blanchard, and Gregory Handy · 2013
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Analysis of learning from positive and unlabeled data
Marthinus C Du Plessis, Gang Niu, and Masashi Sugiyama · 2014
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Understanding the relationship between self-reported offending and official criminal charges across early adulthood
Amanda B Gilman, Karl G Hill, BK Elizabeth Kim, Alyssa Nevell, J David Hawkins, and David P Farrington · 2014
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Handbook of missing data methodology
Geert Molenberghs, Garrett Fitzmaurice, Michael G Kenward, Anastasios Tsiatis, and Geert Verbeke · 2014
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Sensitivity analysis in observational studies
Paul R Rosenbaum · 2014
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How the war on drugs damages black social mobility
Jonathan Rothwell · 2014
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Assessing binary classifiers using only positive and unlabeled data
Marc Claesen, Jesse Davis, Frank De Smet, and Bart De Moor · 2015
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Risk and Needs Assessment in the Criminal Justice System , volume 44087
Nathan James · 2015
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Learning from corrupted binary labels via class-probability estimation
Aditya Menon, Brendan Van Rooyen, Cheng Soon Ong, and Bob Williamson · 2015
Fairness in criminal justice risk assessments: The state of the art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
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Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
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Recovering true classifier performance in positive-unlabeled learning
Shantanu Jain, Martha White, and Predrag Radivojac · 2017
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James E. Johndrow and Kristian Lum · 2017
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Understanding race/ethnicity differences in offending across the life course: Gaps and opportunities
Alex R Piquero · 2015
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A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
Clayton Scott · 2015
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Risk, race, & recidivism: Predictive bias and disparate impact
Jennifer L Skeem and Christopher T Lowenkamp · 2015
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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
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Compas risk scales: Demonstrating accuracy equity and predictive parity
William Dieterich, Christina Mendoza, and Tim Brennan · 2016
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Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf · 2017
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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Learning from positive and unlabeled data under the selected at random assumption
Jessa Bekker and Jesse Davis · 2018
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Towards instance-dependent label noise-tolerant classification: a probabilistic approach
Jakramate Bootkrajang and Jeerayut Chaijaruwanich · 2018
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Classification with imperfect training labels
Timothy I Cannings, Yingying Fan, and Richard J Samworth · 2018
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The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel · 2018
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Empirical risk minimization under fairness constraints
Michele Donini, Luca Oneto, Shai Ben-David, John S Shawe-Taylor, and Massimiliano Pontil · 2018
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Instance-dependent pu learning by bayesian optimal relabeling
Fengxiang He, Tongliang Liu, Geoffrey I Webb, and Dacheng Tao · 2018
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Residual unfairness in fair machine learning from prejudiced data
Nathan Kallus and Angela Zhou · 2018
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Causal reasoning for algorithmic fairness
Joshua R Loftus, Chris Russell, Matt J Kusner, and Ricardo Silva · 2018
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Fair inference on outcomes
Razieh Nabi and Ilya Shpitser · 2018
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A generalized neyman-pearson criterion for optimal domain adaptation
Clayton Scott · 2018
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Statistical analysis with missing data , volume 793
Roderick JA Little and Donald B Rubin · 2019
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