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...
Agentic AI Deserves a Presumption of Authorization
By Zefang (Jeff) Wu - Edited by Min Su KimZefang (Jeff) Wu is Assistant Vice President at GIC Private Limited (“GIC”). He was previously an attorney at Davis Polk & Wardwell LLP. He received his Juris Doctor degree from New York University School of Law and his Bachelor of Science degree from University of California, Berkeley. The views expressed in this Commentary are solely those of the author and do not represent the views of GIC, its affiliates, or its investment activities. Introduction 2026 is the year of agentic...
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.