Algorithms are most helpful when applied to problems where there is not only a large history of past

cases to learn from but also a clear outcome that can be measured, since measuring the outcome

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concretely is a necessary prerequisite to predicting. But a prediction algorithm, on its own, will

focus relentlessly on predicting the outcome you provide as accurately as possible at the expense of

everything else. This creates a danger: if you care about other outcomes too, they will be ignored. So

even if the algorithm does well on the outcome you told it to focus on, it may do worse on the other

outcomes you care about but didn’t tell it to predict.

This concern came up repeatedly in our own work on bail decisions. We trained our algorithms to

predict the overall crime rate for the defendents eligible for bail. Such an algorithm treats every

crime as equal. But what if judges (not unreasonably) put disproportionate weight on whether a

defendant engages in a very serious violent crime like murder, rape, or robbery? It might look like

the algorithm’s predictions leads to “better outcomes” when we look at overall rates of crime. But

the algorithm’s release rule might actually be doing worse than the judges with respect to serious

violent crimes specifically. The possibility of this happening doesn’t mean algorithms can’t still be

useful. In bail, it turns out that different forms of crime are correlated enough so that an algorithm

trained on just one type of crime winds up out-predicting judges on almost every measure of

criminality we could construct, including violent crime. The point is that the outcome you select for

your algorithm will define it. So you need to think carefully about what that outcome is and what

else it might be leaving out.

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