Spring 2026
Featured Article
Majoritarian Signals: Harnessing GenAI to Inform Judicial Standards
Uri Y. Hacohen & Niva Elkin-KorenThis Article presents a systematic framework for incorporating majoritarian signals from generative AI (“GenAI”) foundation models into legal adjudication. While legal scholars have traditionally viewed GenAI’s embedded social biases as a normative flaw, this Article reframes them as potentially valuable evidentiary proxies for interpreting ambiguous or open-ended legal standards. When carefully scrutinized, majoritarian signals — patterns that reflect the most common uses, norms, or expectations in language and culture — can illuminate the shared understandings that underlie core legal doctrines...
Antitrust Principles for Prediction Markets
By Evan Miller - Edited by Min Su Kim and Shriya SrikanthEvan Miller is a Partner at Vinson & Elkins LLP. He focuses his practice on antitrust investigations and counseling with particular emphasis on emerging technologies. The views expressed in this commentary are solely his own and do not necessarily represent the policies or views of Vinson & Elkins LLP. Introduction Businesses are increasingly recognizing prediction market platforms such as Kalshi and Polymarket as sophisticated mechanisms for aggregating dispersed information and generating real-time probabilistic forecasts. [1] Hedge funds, political risk analysts...
Majoritarian Signals: Harnessing GenAI to Inform Judicial Standards
This Article presents a systematic framework for incorporating majoritarian signals from generative AI (“GenAI”) foundation models into legal adjudication. While legal scholars have traditionally viewed GenAI’s embedded social biases as a normative flaw, this Article reframes them as potentially valuable evidentiary proxies for interpreting ambiguous or open-ended legal standards. When carefully scrutinized, majoritarian signals — patterns that reflect the most common uses, norms, or expectations in language and culture — can illuminate the shared understandings that underlie core legal doctrines. Drawing on insights from computational social science, this Article demonstrates how GenAI models trained on vast cultural corpora can capture statistical regularities that mirror prevailing beliefs, practices, and linguistic conventions. These signals, it argues, can help courts approximate the meaning of terms like “reasonable care,” “ordinary meaning,” “genericity,” and “originality” — all standards that frequently rely on implicit majoritarian reasoning but lack reliable empirical tools for application.