Aniket Kesari
Fordham School of Law
Opinions on Opinions: Legal Reasoning and Court Approval
Abstract
Judges in common-law systems write detailed opinions not only to explain their decisions to the parties involved, but also as a form of public accountability. Do the legal arguments in these opinions really bolster institutional legitimacy? We analyze this question in the context of legal arguments made by U.S. Supreme Court judges, where we use legal AI to simplify and decompose complex judicial opinions and produce neutral, readable statements of facts and reasoning. In a well-powered pre-registered survey experiment, we ask whether respondents exposed to judicial reasoning change their attitudes toward the Court and its decisions, relative to control-group respondents who view only the facts and the decision (without the legal reasoning). We find that while exposure to legal reasoning can increase agreement with the Court’s reasoning, it can also trigger a backlash effect among individuals who initially disapprove of the Court, especially in more salient cases. Although respondents exposed to legal reasoning do not significantly shift their views on the Court’s legitimacy, they do become more likely to articulate their views on the court in legal rather than political terms.
About
Aniket Kesari is an Associate Professor at Fordham Law School. His research focuses on law & technology, data science, and public policy. He uses techniques drawn from causal inference, machine learning, and natural language processing to investigate questions in law and tech, and he is also interested in integrating data science into empirical legal studies more broadly.
Some of his recent scholarship looks at data breach notification laws, mandatory cybersecurity risk disclosures, privacy and algorithmic fairness, trademark search engines, and online hate speech. His work has appeared in law reviews (George Washington Law Review, Berkeley Technology Law Journal, Illinois Journal of Law, Technology, and Policy, NYU Journal of Legislation and Public Policy), peer-reviewed social science outlets (Journal of Empirical Legal Studies, Journal of Online Trust and Safety), and peer-reviewed computer science proceedings (Neural Information Processing Systems AI for Social Good Workshop, ACM Symposium on Computer Science and Law).
Prior to joining Fordham in 2023, Aniket was a research fellow at NYU’s Information Law Institute, a postdoc at UC Berkeley’s Social Science D-Lab, and a visiting researcher at ETH Zurich’s Center for Law & Economics. At Berkeley, he co-developed a Data Science, Prediction, and Law undergraduate course, a Computer Programming for Lawyers JD course, and Computational Social Science doctoral course. He was also a Tech Policy Intern at GitHub, a Data Science for Social Good Fellow at the University of Chicago, and a Google Policy Fellow at Engine