Year: 2026
Type: preprint
Abstract: Alignment of artificial intelligence (AI) encompasses the normative problem of specifying how AI systems should act and the technical problem of ensuring AI systems comply with those specifications. To date, AI alignment has generally overlooked an important source of knowledge and practice for grappling with these problems: law. In this paper, we survey the emerging field of legal alignment that aims to fill this gap and systematize research that studies how legal rules, principles, and methods can be leveraged to address problems of alignment and inform the design of AI systems that operate safely and ethically. Our survey provides a taxonomy of the three core research pathways of legal alignment and explores how each can be operationalized in practice: (1) designing AI systems to comply with the content of legal rules developed through legitimate institutions and processes, (2) adapting methods from legal interpretation to guide how AI systems reason and make decisions, and (3) harnessing legal concepts as a structural blueprint for confronting challenges of reliability, trust, and cooperation in AI systems. These research pathways present new conceptual, empirical, and institutional questions, which include examining the specific set of laws that particular AI systems should follow, creating evaluations to assess their legal compliance in real-world settings, and developing governance frameworks to support the implementation of legal alignment in practice. Tackling these questions requires expertise across law, computer science, and other disciplines, offering these communities the opportunity to collaborate in designing AI for the better.
Source: arXiv (Cornell University)
Authors: Kolt, Noam, Caputo, Nicholas, Boeglin, Jack, O'Keefe, Cullen, Bommasani, Rishi, Casper, Stephen, Cuéllar, Mariano-Florentino, Feldman, Noah, Gabriel, Iason, Hadfield, Gillian K., Hammond, Lewis, Henderson, Peter, Kasirzadeh, Atoosa, Lazar, Seth, Reuel, Anka, Wei, Kevin L., Zittrain, Jonathan
Institutions: Hebrew University of Jerusalem, University of Pennsylvania, Stanford University, MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Carnegie Endowment for International Peace, Harvard University, University of London, School of Advanced Study, Johns Hopkins University, Vector Institute, University of Oxford, Princeton University, Carnegie Mellon University, Australian National University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Hebrew University of Jerusalem
OpenAlex ID: W7119503399