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Post-Trial Plea Bargaining and Predictive Analytics in Public Law


Harold J. Krent


February 22, 2017

Adam Gershowitz’s article calling for post-trial plea bargaining in capital cases reasons that governors should commute sentences to life in prison, in exceptional cases, to limit the costs of protracted post-trial litigation over imposition of the death penalty. The commutation power, in his view, resembles pre-trial plea bargaining in that both the state and the criminal defendant can benefit—the state saves resources while the defendant gets off death row.

Gershowitz’s article, therefore, affords a window into the increasing use of predictive analytics in deciding whether to bring or resolve litigation. Sifting through data on all prior capital cases can yield clues as to the likelihood of success or the length of litigation in future capital cases. Not surprisingly, the past can, to some extent, help us predict the future and thereby inform the governor’s commutation decision.

Deployment of predictive analytics is more familiar in the private sector. The life insurance industry historically is predicated on actuarial science, and credit card companies rely on complex data to score riskiness of a loan or to detect fraud. Even sports teams follow a “Moneyball” approach to drafting and acquiring the best talent possible based on prior data.

Gershowitz’s article presages the role that predictive analytics will play in the public sector, saving vast resources and limiting subjectivity in governmental decision-making. Reliance on prior data can help determine when the government should settle torts cases, pay Veterans claims, and subject those receiving disability to review to determine if their disability continues. Predictive analytics may also help the IRS streamline tax auditing and collection. On the other hand, unlike in private law, individuated decision-making may be required by the government either under the Constitution or legislative directives. Moreover, the government’s consideration of historical factors correlated with protected categories such as race may result, on occasion, in discrimination when reliance on the prior data culminates in denial of a benefit or increased punishment. As with any other technological breakthrough, predictive analytics as applied to the public sector brings tremendous promise but concerns as well.