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| Fairly assessing risk and recidivism |
| By baltimoresun.com - Dawinder Sidhu |
| Published: 03/27/2015 |
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The use of big data to track and analyze human behavior has crept into perhaps the most consequential and sensitive of contexts in society — criminal sentencing. While predictions of future criminality have thus far been made primarily through clinical assessments, often involving in-person interviews, the criminal justice system is turning to analytics to make these predictions. Under this actuarial approach, an individual's risk of recidivism is computed by aggregating the rates of recidivism for individuals possessing the same group characteristics. For example, the risk profile of a poor high-school educated white man will be calculated according to the rates of recidivism for individuals with the same socio-economic, educational, race and gender characteristics. At least 20 states have adopted this "Moneyball" approach in sentencing. Influential legal organizations have endorsed these risk-assessment tools as well. Moreover, members of Congress have proposed bipartisan legislation that would require the Department of Justice to develop a risk-assessment instrument that could help identify low-risk prisoners who could earn early release into community-based or home confinement. Read More. |
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