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Computerized Criminal Behavior Predictions Are No More Effective Than Untrained Humans: Report

February 13, 2018 in Blogs

By Rochaun Meadows-Fernandez, AlterNet

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Without effective scrutiny, algorithm-based software could hurt those who are already the most vulnerable.

The effectiveness of the criminal justice system has been debated since its creation. There is great difficulty in developing a uniform system when criminal defendants’ circumstances are variable. Thanks to recent coverage of police shooting, sexual assault cases and self-defense trials over the last few years, the criminal justice system has become interwoven with our daily news of politics, government and pop culture. It doesn’t take long to see the system operates in favor of those with power and influence while being disadvantageous for those with a history of systemic vulnerability. It is inescapable, and it is becoming increasingly apparent that the system is flawed.

We had hoped that in the age of technology, we could eradicate bias by putting computer programs in place of our old systems. With algorithm-based systems, we can make faster, less variable predictions about the likelihood of people ending up in the criminal justice system again, or recidivism. But it’s become increasingly apparent that automating the process made things worse because now we have taken old bias and embedded it by teaching it to computers. We hoped machines could provide the fair treatment humans have failed to give criminal defenders and past offenders—but they haven’t. And it turns out, machines may not be any more effective than humans at predicting recidivism.

“People hear words like ‘big data’ and ‘machine learning’ and often assume that these methods are both accurate and unbiased simply because of the amount of data used to build them,” said Julia Dressel, whose senior undergraduate honors thesis in computer science at Dartmouth College is gaining national attention.

Earlier this year, Dressel released a report in conjunction with computer science professor Hany Farid …read more


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