Algorithms that predict crime are watching – and judging us by the cards we’ve been dealt

By Tatiana Dancy, The University of Melbourne

Your money, postcode, friends and family can make all the difference to how the criminal system treats you.

The New South Wales police recently scrapped a widely condemned program known as the Suspect Targeting Management Plan. It used algorithmic risk scores to single out “targets”, some as young as ten years old, for police surveillance.

But similar programs remain in place. For instance, Corrective Services NSW uses a statistical assessment tool called LSI-R to predict whether prisoners will reoffend.

“High risk” prisoners receive “high intensity interventions”, and may be denied parole. The risk scores are calculated from facts such as “criminal friends”, family involvement in crime or drugs, financial problems, living in a “high crime neighbourhood” and frequent changes of address.

A predictive algorithm is a set of rules for computers (and sometimes people) to follow, based on patterns in data. Lots has been written about how algorithms discriminate against us, from biased search engines to health databases.

In my newly published book, Artificial Justice, I argue the use of tools that predict our behaviour based on factors like poverty or family background should worry us, too. If we are punished at all, it should be only for what we have done wrong, not for the cards we have been dealt.

Algorithms are watching us

Algorithms generate risk scores used in criminal justice systems all over the world. In the United Kingdom, the OASys (Offender Assessment System) is used as part of the pre-sentence information given to judges – it shapes bail, parole and sentencing decisions. In the United States, a tool known as COMPAS does something similar.

Risk scores are used beyond criminal justice, too, and they don’t always need computers to generate them. A short survey known as the Opioid Risk Tool helps doctors in Australia and across the world decide whether to prescribe pain relief for acute and chronic illness, by predicting whether patients will misuse their medications.

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