A Replacement for Justitia's Scales? Machine Learning's Role in Sentencing

By Donohue, Michael E. | Harvard Journal of Law & Technology, Spring 2019 | Go to article overview

A Replacement for Justitia's Scales? Machine Learning's Role in Sentencing


Donohue, Michael E., Harvard Journal of Law & Technology


TABLE OF CONTENTS    I. INTRODUCTION                                              657  II. THE MACHINE AS MANIPULATOR                                660      A. Recidivism-Risk Tools                                  660      B. The Risk of Anchoring on a Recidivism Score            661         1. Objections to Population-Level Input Data           662         2. Objections to an Opaque and Proprietary Tool        664         3. Objections to Anchoring on a Single Philosophy of   665            Punishment III. THE MACHINE AS MASTER                                     667      A. The U.S. Sentencing Guidelines                         667      B. The Trouble with Replacing Discretion                  669  IV. THE MACHINE AS MENTOR                                     672      A. An SIS-Enhanced Common Law of Sentencing               673      B. A Machine-Learning-Powered Dialog                      675         1. Mitigating Cognitive Biases                         676         2. Enabling Conversation                               677   V. CONCLUSION                                                678 

I. INTRODUCTION

TO BELIEVE THAT THIS JUDICIAL EXERCISE OF JUDGMENT COULD BE AVOIDED BY  FREEZING "DUE PROCESS OF LAW"... IS TO SUGGEST THAT THE MOST IMPORTANT  ASPECT OF CONSTITUTIONAL ADJUDICATION IS A FUNCTION FOR INANIMATE  MACHINES AND NOT FOR JUDGES.... EVEN CYBERNETICS HAS NOT YET MADE THAT  HAUGHTY CLAIM. --JUSTICE FELIX FRANKFURTER (1) 

Criminal sentencing is one of the most difficult responsibilities of judging. (2) It is a different sort of task than the others that face a judge; unlike deciding upon motions or policing the arguments of counsel, sentencing comes down to a singular moment of moral judgment shared between the robed jurist and the defendant standing before the bench. (3)

The task of sentencing is hard because judges face multiple and conflicting instructions from the legislature and society. The sentence must exact proportional retribution for the wrong committed. It must deter the defendant from offending again, as well as others from offending in the first place. The sentence must be long enough to protect society from danger. And, perhaps, the sentence must be of a suitable length and type to rehabilitate the defendant for re-entry into society after punishment. (4) Only occasionally do these instructions point in the same direction, and one judge's interpretation of where they point will differ from others', threatening uniformity across chambers and jurisdictions. As an additional complicating factor, the judge, often a lawyer by training, has limited information about the defendant and the crime in question. At the time of sentencing, the judge will have only experienced a handful of hearings, including, if there is no plea agreement, a trial focused on determining guilt; the judge has even less information on the impact of any possible sentence. (5)

To ease this process--and to ensure to some degree that the judiciary acts as an agent of the legislature's will--legislatures have created a number of tools to quantify the punishment any given defendant deserves. Some have promulgated guidelines as a framework--or mandate--for judges to use in sentencing, (6) and researchers have recommended evidence-based sentencing practices to better understand which defendants are most likely to pose a future danger to society.

Some have sought to apply the latest capabilities in data analysis and processing--machine learning--to this task. (7) Despite the promises of these techniques and technologies, however, all have met with criticism from both defendants and judges. (8)

What explains the criticism for these tools, especially the ones based on machine learning? After all, they have the capability to dispassionately apply the law in every case. They can be, at least theoretically, programmatically blinded to factors that are impermissible to consider. (9) Legislatures can imbue these tools with precise weights and algorithms for consideration of the facts. …

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A Replacement for Justitia's Scales? Machine Learning's Role in Sentencing
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