Risk and Needs Assessment: Constitutional and Ethical Challenges

Hamilton, Melissa

doi:10.2139/ssrn.2506397

Cited by 23 publications

(32 citation statements)

References 19 publications

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“…Risk assessment in criminal justice is about predicting an individual's potential for criminal offending in the future. 15 Risk assessment practices have typically advised back-end options (e.g., parole, post-incarceration supervision). 16 Yet predictions about risk can inform in pretrial settings as well, such as determinations about pretrial release.…”

Section: Pretrial Risk Assessmentmentioning

confidence: 99%

The sexist algorithm

Hamilton

2019

Behavioral Sci & The Law

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Algorithmic risk assessment tools are informed by scientific research concerning which factors are predictive of recidivism and thus support the evidence‐based practice movement in criminal justice. Automated assessments of individualized risk (low, medium, high) permit officials to make more effective management decisions. Computer‐generated algorithms appear to be objective and neutral. But are these algorithms actually fair? The focus herein is on gender equity. Studies confirm that women typically have far lower recidivism rates than men. This differential raises the question of how well algorithmic outcomes fare in terms of predictive parity by gender. This essay reports original research using a large dataset of offenders who were scored on the popular risk assessment tool COMPAS. Findings indicate that COMPAS performs reasonably well at discriminating between recidivists and non‐recidivists for men and women. Nonetheless, COMPAS algorithmic outcomes systemically overclassify women in higher risk groupings. Multiple measures of algorithmic equity and predictive accuracy are provided to support the conclusion that this algorithm is sexist.

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Section: Pretrial Risk Assessmentmentioning

confidence: 99%

The sexist algorithm

Hamilton

2019

Behavioral Sci & The Law

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“…These concerns are not unique to random forests, nor do they rest upon any characteristic of that procedure. Their very generality underscores the need for a thorough airing, 13 but that would take us far beyond the scope of this venue. Moreover, it may well turn out that the challenges are more provocative than compelling.…”

Section: Some Common Misunderstandingsmentioning

confidence: 99%

Machine Learning Forecasts of Risk to Inform Sentencing Decisions

Berk

Hyatt

2015

Federal Sentencing Reporter

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“…There is also a small but growing literature addressing more directly the ethnical and legal issues (Hyatt et al ; Tonry ; Ferguson ; Hamilton ; Barocas & Selbst ; Janssan & Kuk ; Kroll et al forthcoming). Many important matters are addressed, but not at the level of detail required by this article.…”

Section: Introductionmentioning

confidence: 99%

“…There is also a small but growing literature addressing more directly the ethnical and legal issues (Hyatt et al 2011;Tonry 2014;Ferguson 2015;Hamilton 2016;Barocas & 1 The data were collected as part of a demonstration of concept. The demonstration went well, but was also an implicit criticism of existing practices.…”

Section: Introductionmentioning

confidence: 99%

Accuracy and Fairness for Juvenile Justice Risk Assessments

Berk¹

2019

J Empirical Legal Studies

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Risk assessment algorithms used in criminal justice settings are often said to introduce “bias.” But such charges can conflate an algorithm's performance with bias in the data used to train the algorithm with bias in the actions undertaken with an algorithm's output. In this article, algorithms themselves are the focus. Tradeoffs between different kinds of fairness and between fairness and accuracy are illustrated using an algorithmic application to juvenile justice data. Given potential bias in training data, can risk assessment algorithms improve fairness and, if so, with what consequences for accuracy? Although statisticians and computer scientists can document the tradeoffs, they cannot provide technical solutions that satisfy all fairness and accuracy objectives. In the end, it falls to stakeholders to do the required balancing using legal and legislative procedures, just as it always has.

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Risk and Needs Assessment: Constitutional and Ethical Challenges

Cited by 23 publications

References 19 publications

The sexist algorithm

The sexist algorithm

Machine Learning Forecasts of Risk to Inform Sentencing Decisions

Accuracy and Fairness for Juvenile Justice Risk Assessments

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