Race science? Can AI ‘predict’ criminality through facial analysis?

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facial recognition
Credit: Damir Sagolj/Reuters

With “80 percent accuracy and with no racial bias,” the paper, A Deep Neural Network Model to Predict Criminality Using Image Processing, claimed its algorithm could predict “if someone is a criminal based solely on a picture of their face.” The press release has since been deleted from the [Harrisburg University] website.

[June 23], more than 1,000 machine-learning researchers, sociologists, historians, and ethicists released a public letter condemning the paper, and Springer Nature confirmed on Twitter it will not publish the research.

But the researchers say the problem doesn’t stop there… The letter argues it is impossible to predict criminality without racial bias, “because the category of ‘criminality’ itself is racially biased.”

Advances in data science and machine learning have led to numerous algorithms in recent years that purport to predict crimes or criminality. But if the data used to build those algorithms is biased, the algorithms’ predictions will also be biased. Because of the racially skewed nature of policing in the US, the letter argues, any predictive algorithm modeling criminality will only reproduce the biases already reflected in the criminal justice system.

Related article:  Pushing for greater diversity in genetic databases by ending 'cycle of disengagement'

“Like computers or the internal combustion engine, AI is a general-purpose technology that can be used to automate a great many tasks, including ones that should not be undertaken in the first place,” the letter reads.

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