AISEM Presents

When Data Don't Speak:  Empirical Legal Research and the Example of Bankruptcy Examiners

Jonathan Lipson
Foley & Lardner Professor of Law

With the advent of modern computer and information technologies, legal scholarship has undergone a deep transformation. Today, a large and growing number of legal academics conduct "empirical" scholarship driven by data about individual cases gathered and analyzed using conventional social science techniques. While this form of analysis can provide powerful insight into what happens "on the ground", legal academics are hamstrung because their data are often from sources not intended to be used for data analysis (e.g., dockets of pleadings in cases); may involve multiple data sets with (many) different variables; may have missing variables; will often be text (rather than numerically) based; will often involve skewness or low statistical power; and so on. In short, legal academics attempt to use data not meant to be analyzed in these ways, and which therefore do not "speak" as clearly to the problems we study as we would hope.

The goal of this talk is to explain challenges encountered in a particular data-driven project (the pattern in the use of "examiners" in large corporate bankruptcies) and the limits of conventional regression analysis in this context, with a view toward describing more generally the kinds of machine-learning and artificial intelligence tools that could aid legal scholarship.

Click here to view a recent paper by the speaker on the topic: Understanding Failure: Examiners and the Bankruptcy Reorganization of Large Public Companies

Click here for a link to Prof. Lipson's biography.

Light refreshments will be served at 2:45pm.

This talk is part of the AI In the Wild series.  For more information, including upcoming talks, click here.