Human Decisions and Machine Predictions

Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig and Sendhil Mullainathan

The Quarterly Journal of Economics, 2018, vol. 133, issue 1, 237-293

Abstract: Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are 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 variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. 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: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; these gains can be achieved while simultaneously reducing racial disparities. 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.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (150)

Downloads: (external link)
http://hdl.handle.net/10.1093/qje/qjx032 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Human Decisions and Machine Predictions (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:qjecon:v:133:y:2018:i:1:p:237-293.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

The Quarterly Journal of Economics is currently edited by Robert J. Barro, Lawrence F. Katz, Nathan Nunn, Andrei Shleifer and Stefanie Stantcheva

More articles in The Quarterly Journal of Economics from President and Fellows of Harvard College
Bibliographic data for series maintained by Oxford University Press ().