Empirical Legal Studies

CELS 2023 -- Conference Registration

2023 Conference on Empirical Legal Studies

October 13-14, 2023

Conference Registration

University of Chicago Law School

The University of Chicago Law School and the Society for Empirical Legal Studies (SELS) are pleased to invite you to CELS 2023. The conference will take place October 13–14, 2023, at the University of Chicago’s flagship conference facility, the David Rubenstein Forum. CELS 2023 will feature more than 40 panels, a poster session, and a plenary session on the future of machine learning and AI in empirical legal studies.

As well, on October 12, 2023, UChicago Law and SELS will host a pre-conference workshop on empirical methods: “How to Use Machine Learning and AI in Empirical Legal Research.” This hands-on workshop will feature leading scholars in the field and will introduce participants to tools and techniques for ML- and AI-assisted empirical research.

CELS 2023 Registration: Please visit the CELS 2023 conference website (click here) for more information and to register.

Please direct any conference questions to the conference organizers at: [email protected].

Plotting Kernal Density Estimates as Colored Areas

By default, kernel density estimates are plotted as lines, meaning curves:

According to recent StataList discussions (see here and here), however, plotting kernal density estimates as "areas" has become the standard in some fields. While for publication purposes the coding requires color, if such a possibility exists the resultant information conveyed is quite helpful as the following example illustrates.

SCOTUS Nominations, By the Nimbers and Over Time

Charles Cameron (Princeton--politics ) & Jonathan Kastellec (Princeton--politics) recently published a book, Making the Supreme Court (OUP), that tracks 90 years of POTUS appointments to the Supreme Court. The Book's description (below) makes clear what the authors attempt to achieve with their book. The authors' execution benefits from data, including on:

• Party and presidential interest in the court, including the use of “litmus tests” for Supreme Court nominees;
• The type of selection processes employed by the president;
• The background characteristics of Supreme Court nominees, and those who made the president’s “short list”;
• Interest group participation in Supreme Court confirmation battles;
• Media coverage of Supreme Court nominees;
• Public opinion of nominees;
• Senate roll call voting on Supreme Court nominees; and
• Simulations on the future ideological trajectory of the Supreme Court.

But what particularly caught my eye was that the authors make available the data (and related coding) that they used in their analyses (click here).

“Appointments to the United States Supreme Court are now central events in American political life. Every vacancy unleashes a bitter struggle between Republicans and Democrats over nominees; and once the seat is filled, new justices typically vote in predictable ways. Making the Supreme Court examines 90 years of American political history to show how the growth of federal judicial power from the 1930s onward inspired presidents, the political parties, and interest groups to shape judicial policy through appointments. The result is a new politics aimed squarely at selecting and placing judicial ideologues on the Court, which has transformed the Court into an ideologically driven and polarized branch. Based on rich data and qualitative evidence, Making the Supreme Court provides a sharp lens on the social and political transformations that created a new American politics.”

Levering AI in the Service of Contract Interpretation

Contract interpretation canons and theories continue to evolve. The latest foray seeks to lever AI.

In a recent article, Generative Interpretation, the authors, David Hoffman (Penn) and Yonathan Arbel (Alabama), advance a new approach designed to assist in determining contracting parties’ meaning. The authors' impulse behind generative interpretation, is, according to them, "simple:" they apply large language models (LLMs) to contractual texts and extrinsic evidence to predict what the parties would have said at contracting about what they meant. The paper's abstract follows.

“We introduce generative interpretation, a new approach to estimating contractual meaning using large language models. As AI triumphalism is the order of the day, we proceed by way of grounded case studies, each illustrating the capabilities of these novel tools in distinct ways. Taking well-known contracts opinions, and sourcing the actual agreements that they adjudicated, we show that AI models can help factfinders ascertain ordinary meaning in context, quantify ambiguity, and fill gaps in parties’ agreements. We also illustrate how models can calculate the probative value of individual pieces of extrinsic evidence.

After offering best practices for the use of these models given their limitations, we consider their implications for judicial practice and contract theory. Using LLMs permits courts to estimate what the parties intended cheaply and accurately, and as such generative interpretation unsettles the current interpretative stalemate. Their use responds to efficiency-minded textualists and justice-oriented contextualists, who argue about whether parties will prefer cost and certainty or accuracy and fairness. Parties—and courts—would prefer a middle path, in which adjudicators strive to predict what the contract really meant, admitting just enough context to approximate reality while avoiding unguided and biased assimilation of evidence. As generative interpretation offers this possibility, we argue it can become the new workhorse of contractual interpretation.”