Can you make AI fairer than a judge? Play our courtroom algorithm game — from by Karen Hao and Jonathan Stray
Play our courtroom algorithm game The US criminal legal system uses predictive algorithms to try to make the judicial process less biased. But there’s a deeper problem.


As a child, you develop a sense of what “fairness” means. It’s a concept that you learn early on as you come to terms with the world around you. Something either feels fair or it doesn’t.

But increasingly, algorithms have begun to arbitrate fairness for us. They decide who sees housing ads, who gets hired or fired, and even who gets sent to jail. Consequently, the people who create them—software engineers—are being asked to articulate what it means to be fair in their code. This is why regulators around the world are now grappling with a question: How can you mathematically quantify fairness? 

This story attempts to offer an answer. And to do so, we need your help. We’re going to walk through a real algorithm, one used to decide who gets sent to jail, and ask you to tweak its various parameters to make its outcomes more fair. (Don’t worry—this won’t involve looking at code!)

The algorithm we’re examining is known as COMPAS, and it’s one of several different “risk assessment” tools used in the US criminal legal system.


But whether algorithms should be used to arbitrate fairness in the first place is a complicated question. Machine-learning algorithms are trained on “data produced through histories of exclusion and discrimination,” writes Ruha Benjamin, an associate professor at Princeton University, in her book Race After Technology. Risk assessment tools are no different. The greater question about using them—or any algorithms used to rank people—is whether they reduce existing inequities or make them worse.


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