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...
AI, Confidentiality, and the Stratified Legal Profession
By Steve Leben - Edited by Min Su KimSteve Leben is the Douglas R. Stripp Distinguished Professor of Law and Associate Dean for Faculty at the University of Missouri-Kansas City School of Law, where his courses include Professional Responsibility. He served as a state trial and appellate judge for more than two decades, including on the Kansas Court of Appeals from 2007 to 2020. I. The New Fairness Problem In March 2026, a federal court in Colorado ordered both sides in an employment lawsuit to stop uploading confidential...
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.