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 Identity Gap: Why Executive Order 14306 Needs a Legislative Answer
By Faran Kiani - Edited by Min Su KimFaran Kiani is a technology lawyer, author and Senior Manager, Contracts and Corporate Affairs at Soloinsight, Inc., where he leads technology transactions, data privacy, AI governance, and IP. He holds an LL.M. in Technology Law from Wake Forest University and a certificate in “AI and Law: Navigating the New Legal Landscape” from Harvard Law School’s Executive Education Program. In cybersecurity, the most consequential question is often the simplest one: who is allowed in? The discipline that answers it is identity...
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.