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Misdemeanors By The Numbers, Sandra G. Mayson, Megan T. Stevenson
Misdemeanors By The Numbers, Sandra G. Mayson, Megan T. Stevenson
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Recent scholarship has underlined the importance of criminal misdemeanor law enforcement, including the impact of public-order policing on communities of color, the collateral consequences of misdemeanor arrest or conviction, and the use of misdemeanor prosecution to raise municipal revenue. But despite the fact that misdemeanors represent more than three-quarters of all criminal cases filed annually in the United States, our knowledge of misdemeanor case processing is based mostly on anecdote and extremely localized research. This Article represents the most substantial empirical analysis of misdemeanor case processing to date. Using multiple court-record datasets, covering several million cases across eight diverse jurisdictions, …
Bias In, Bias Out, Sandra G. Mayson
Bias In, Bias Out, Sandra G. Mayson
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Police, prosecutors, judges, and other criminal justice actors increasingly use algorithmic risk assessment to estimate the likelihood that a person will commit future crime. As many scholars have noted, these algorithms tend to have disparate racial impacts. In response, critics advocate three strategies of resistance: (1) the exclusion of input factors that correlate closely with race; (2) adjustments to algorithmic design to equalize predictions across racial lines; and (3) rejection of algorithmic methods altogether.
This Article’s central claim is that these strategies are at best superficial and at worst counterproductive because the source of racial inequality in risk assessment lies …
Strong Claims And Weak Evidence: Reassessing The Predictive Validity Of The Iat, Hart Blanton, James Jaccard, Jonathan Klick, Barbara Mellers, Gregory Mitchell, Philip Tetlock
Strong Claims And Weak Evidence: Reassessing The Predictive Validity Of The Iat, Hart Blanton, James Jaccard, Jonathan Klick, Barbara Mellers, Gregory Mitchell, Philip Tetlock
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The authors reanalyzed data from 2 influential studies — A. R. McConnell and J. M. Leibold (2001) and J. C. Ziegert and P. J. Hanges (2005) — that explore links between implicit bias and discriminatory behavior and that have been invoked to support strong claims about the predictive validity of the Implicit Association Test. In both of these studies, the inclusion of race Implicit Association Test scores in regression models reduced prediction errors by only tiny amounts, and Implicit Association Test scores did not permit prediction of individual-level behaviors. Furthermore, the results were not robust when the impact of rater …