We examine how machine learning can be used to improve and understand human decision-making. In particular, we focus on a decision that has important policy consequences. Millions of times each year, judges must decide where defendants will await trial—at home or in jail. By law, this decision hinges on the judge’s prediction of what the defendant would do if released. This is a promising machine learning application because it is a concrete prediction task for which there is a large volume of data available. Yet comparing the algorithm to the judge proves complicated. First, the data are themselves generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the single variable that the algorithm focuses on; for instance, judges may care about racial inequities or about specific crimes (such as violent crimes) rather than just overall crime risk. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: a policy simulation shows crime can be reduced by up to 24.8% with no change in jailing rates, or jail populations can be reduced by 42.0% with no increase in crime rates. Moreover, we see reductions in all categories of crime, including violent ones. Importantly, such gains can be had while also significantly reducing the percentage of African-Americans and Hispanics in jail. We find similar results in a national dataset as well. In addition, by focusing the algorithm on predicting judges’ decisions, rather than defendant behavior, we gain some insight into decision-making: a key problem appears to be that judges to respond to ‘noise’ as if it were signal. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.
Blogs I Follow
- What's going on in Ghana libraries?
- Sending books to Burkina Faso
- Reading Fati and the Honey Tree in Sumbrungu library, Ghana
- Recent activities in Ghana libraries
- SSNIT pension payments (social security) for librarians in Ghana
- Visites des élèves aux bibliothèques au nord du Burkina Faso
- Students use Ghana libraries to study for end of year exams
- FAVL finalist for Systematic Joy of Reading Award!
- L'excellence récompensée à FAVL
- Achat de livres pour la bibliothèque de Niankorodougou
- FAVL expenses in Ghana from January through April 2017
- Echos de Niankorodougou
- Une bonne information du côté de la commune de Tongomayel
- La nouvelle : Sur les sentiers épineux de la démocratie
- Le coordonnateur national de FAVL-BF à Conakry Capitale Mondiale du Livre