May It Please the Bot?
MIT Computational Law Report, Aug. 14 2020
12 Pages Posted: 9 Oct 2020
Date Written: August 1, 2020
Imagine a system where appellate attorneys could be virtually assured of how a panel of judges would decide what otherwise appears to be a close case. Imagine how the work of an overburdened legal aid attorney could be lightened if she could accurately predict the exact amount of fees that a judge would award her client in an eviction case. Imagine the benefits to a law firm that could have paralegals write the first drafts of important motions, citing only the cases preferred by the local magistrate. Finally, imagine how the work of writing judicial opinions would become easier for a judge if she was able to recognize the framework the Supreme Court would use to decide whether her decision should be overturned. These are only some of the benefits we envision for re-thinking the ways that judges write judicial opinions and encouraging the adoption of methods that better utilize modern technologies.
The American judiciary plays an integral role in defining and upholding the law. For decades, the legal informatics community has sought to use information technology to search, analyze, and make predictions based on large corpora of judicial opinions. Unfortunately, while these data-driven technologies have made significant progress, they face a lingering limitation: the language and structure of the opinions themselves. Judicial opinions — particularly appellate decisions — lack common standards for language, structure, and conveying critical information about the decision. Some will see this as a feature, not a bug. However, because judicial decisions come in many forms and styles, it is up to lawyers and courts to tease out essential elements of past decisions: e.g., holdings, tests, relative weights of factors.
In this essay, we argue that judges should write opinions in anticipation of later machine processing, and that in doing so they can increase the efficiency and predictability of the legal system. Below, we lay out our theory for why this is within grasp, identify the challenges we expect along the way, and describe approaches we envision.
Suggested Citation: Suggested Citation