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2018 ; 133
(1
): 237-293
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HUMAN DECISIONS AND MACHINE PREDICTIONS
#MMPMID29755141
Kleinberg J
; Lakkaraju H
; Leskovec J
; Ludwig J
; Mullainathan S
Q J Econ
2018[Feb]; 133
(1
): 237-293
PMID29755141
show ga
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; and 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. JEL Codes: C10 (Econometric and
statistical methods and methodology), C55 (Large datasets: Modeling and
analysis), K40 (Legal procedure, the legal system, and illegal behavior).
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