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...
The New Gatekeepers: How AI Chat Interfaces Block People From Their Banks, Insurers, and Student Loan Servicers
By Stephanie T. Nguyen and Adam Teitelbaum - Edited by Min Su KimStephanie T. Nguyen is a Senior Fellow at Columbia Law School’s Center for Law and the Economy and author of the Division of Field Notes. She was the former Chief Technologist at the FTC under Chair Lina Khan. Adam Teitelbaum is a Senior Fellow at Protect Borrowers, and is the former Director of the Office of Consumer Protection at the Office of the Attorney General for the District of Columbia. All errors are the authors. We are grateful to the...
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.