Nums: How likely is it that a criminal will do it again?

In a prior post Feds: “Hire ex-cons … we do” … say, what? … we reported that Feds are hiring ex-cons into the State Department … and pressuring private companies to hire them, too.

Sounds risky to me, but what if there was some objective way to cut the risk … to determine the likelihood that an ex-con would (or would not) go straight.

 

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There is a way.

Some courts and parole departments are using predictive analytics to help decide who belongs in prison.

Here’s the scoop …

According to Eric Siegel …

Law enforcement’s deployment of predictive analytics is building steam.

Philadelphia’s Adult Probation and Parole Department enlisted a Penn professor of statistics and criminology to build models predicting whether parole candidates were recidivism risks.

“Our vision was that every single person, when they walked through the door [of a parole hearing], would be scored by a computer” as to his or her risk of recidivism— committing crime again.

Similarly, Oregon launched a crime prediction tool to be consulted by judges when sentencing convicted felons.

The tool predicts the probability that an offender offender will be convicted again for a felony within three years of being released.

The model was developed by analyzing the records of 55,000 Oregon offenders across five years of data.

The model then validated across 350,000 offender records across 30 years of history.

Among the least risky tenth of criminals — those for whom the model outputs the lowest predictive scores— recidivism is just 20 percent.

Yet among the top fifth receiving the highest scores, recidivism will probably occur; over half of these offenders will commit a felony again.

In these “deployments”, predictive analytics “builds upon and expands beyond a longstanding tradition of crime statistics and standard actuarial models” to enable more objective, fact-based decision-making.

But. the stakes are high since miscalculations in the legal  arena are more costly than for other applications of predictive analytics.

Lives are literally held in the balance.

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Source: Siegel, Eric (2013-02-07). Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Wiley. Kindle Edition.

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