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Human Decisions and Machine Predictions

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Listed:
  • Jon Kleinberg
  • Himabindu Lakkaraju
  • Jure Leskovec
  • Jens Ludwig
  • Sendhil Mullainathan

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.

Suggested Citation

  • Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, Oxford University Press, vol. 133(1), pages 237-293.
  • Handle: RePEc:oup:qjecon:v:133:y:2018:i:1:p:237-293.
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    1. Jeffrey R. Kling, 2006. "Incarceration Length, Employment, and Earnings," American Economic Review, American Economic Association, vol. 96(3), pages 863-876, June.
    2. Mendel, Brock & Shleifer, Andrei, 2012. "Chasing noise," Journal of Financial Economics, Elsevier, vol. 104(2), pages 303-320.
    3. Will Dobbie & Jacob Goldin & Crystal Yang, 2016. "The Effects of Pre-Trial Detention on Conviction, Future Crime, and Employment: Evidence from Randomly Assigned Judges," Working Papers id:11212, eSocialSciences.
    4. Rafael Di Tella & Ernesto Schargrodsky, 2013. "Criminal Recidivism after Prison and Electronic Monitoring," Journal of Political Economy, University of Chicago Press, vol. 121(1), pages 28-73.
    5. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    6. Will Dobbie & Jacob Goldin & Crystal Yang, 2016. "The Effects of Pre-Trial Detention on Conviction, Future Crime, and Employment: Evidence from Randomly Assigned Judges," Working Papers 601, Princeton University, Department of Economics, Industrial Relations Section..
    7. Pedro Bordalo & Nicola Gennaioli & Andrei Shleifer, 2012. "Salience Theory of Choice Under Risk," The Quarterly Journal of Economics, Oxford University Press, vol. 127(3), pages 1243-1285.
    8. Anna Aizer & Joseph J. Doyle, 2015. "Juvenile Incarceration, Human Capital, and Future Crime: Evidence from Randomly Assigned Judges," The Quarterly Journal of Economics, Oxford University Press, vol. 130(2), pages 759-803.
    9. Arpit Gupta & Christopher Hansman & Ethan Frenchman, 2016. "The Heavy Costs of High Bail: Evidence from Judge Randomization," The Journal of Legal Studies, University of Chicago Press, vol. 45(2), pages 471-505.
    10. Shiller, Robert J, 1981. "Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?," American Economic Review, American Economic Association, vol. 71(3), pages 421-436, June.
    11. Will Dobbie & Jacob Goldin & Crystal Yang, 2016. "The Effects of Pre-Trial Detention on Conviction, Future Crime, and Employment: Evidence from Randomly Assigned Judges," NBER Working Papers 22511, National Bureau of Economic Research, Inc.
    12. David S. Abrams & Chris Rohlfs, 2011. "Optimal Bail And The Value Of Freedom: Evidence From The Philadelphia Bail Experiment," Economic Inquiry, Western Economic Association International, vol. 49(3), pages 750-770, July.
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    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • H0 - Public Economics - - General
    • K0 - Law and Economics - - General

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