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Human Decisions and Machine Predictions

Listed author(s):
  • Jon Kleinberg
  • Himabindu Lakkaraju
  • Jure Leskovec
  • Jens Ludwig
  • Sendhil Mullainathan

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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 23180.

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Date of creation: Feb 2017
Handle: RePEc:nbr:nberwo:23180
Note: LE LS PE
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  1. 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..
  2. Brock Mendel & Andrei Shleifer, "undated". "Chasing Noise," Working Paper 19517, Harvard University OpenScholar.
  3. 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.
  4. 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.
  5. 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.
  6. Jeffrey R. Kling, 2004. "Incarceration Length, Employment, and Earnings," Working Papers 873, Princeton University, Department of Economics, Industrial Relations Section..
  7. 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.
  8. Pedro Bordalo & Nicola Gennaioli & Andrei Shleifer, 2010. "Salience Theory of Choice Under Risk," NBER Working Papers 16387, National Bureau of Economic Research, Inc.
  9. 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.
  10. 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, 07.
  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. 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.
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