Fetching the paper…
Reading the bibliography…
Recidivism prediction instruments provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time.
Clinical versus statistical prediction: A theoretical analysis and a review of the evidence
Paul E Meehl · 1954
Earlier work this paper cites.
Statistical Power Analysis for the Behavioral Sciences (2nd Edition)
Jacob Cohen · 1988
Earlier work this paper cites.
The influence of race on sentencing: A meta-analytic review of experimental studies
Laura T Sweeney and Craig Haney · 1992
Earlier work this paper cites.
Clinical versus mechanical prediction: a meta-analysis
William M Grove, David H Zald, Boyd S Lebow, Beth E Snitz, and Chad Nelson · 2000
Earlier work this paper cites.
Validation of the compas risk assessment classification instrument
Thomas Blomberg, William Bales, Karen Mann, Ryan Meldrum, and Joe Nedelec · 2010
Cited alongside, same era.
An overview of the federal post conviction risk assessment, September 2011
Administrative Office of the United States Courts · 2011
Cited alongside, same era.
Testing for racial prejudice in the parole board release process: Theory and evidence
Shamena Anwar and Hanming Fang · 2012
Cited alongside, same era.
Predictive validity performance indicators in violence risk assessment: A methodological primer
Jay P Singh · 2013
Cited alongside, same era.
Should prison sentences be based on crimes that haven’t been committed yet?
Ben Casselman Anna Maria Barry-Jester and Dana Goldstein
Cited in the paper.
Compas risk & need assessment system: Selected questions posed by inquiring agencies
Northpointe
Cited in the paper.
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
Cited in the paper.
How we analyzed the compas recidivism algorithm
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner
Cited in the paper.
Risk, race, & recidivism: Predictive bias and disparate impact
Jennifer L Skeem and Christopher T Lowenkamp · 2015
Later among the works it cites.
Gender, risk assessment, and sanctioning: The cost of treating women like men
Jennifer L Skeem, John Monahan, and Christopher T Lowenkamp · 2016
Closest in time.
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.”
Anthony W Flores, Kristin Bechtel, and Christopher T Lowenkamp · 2016
Closest in time.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…