Legal Alignment for Safe and Ethical AI

TL;DR

The paper argues that legal frameworks offer a valuable lower bound and practical toolkit for AI alignment, addressing both normative content and technical decision-making. It introduces legal alignment as three complementary pathways: following substantive law, applying legal interpretation to AI reasoning, and using legal institutions as structural blueprints for reliability and cooperation. It outlines concrete implementation avenues—empirical evaluations, technical interventions, and institutional frameworks—while acknowledging open questions about the nature of law, edge cases, and future scalability. The work emphasizes the legitimacy, transparency, and adaptability of law as a governance scaffold for safe and ethical AI, and calls for cross-disciplinary collaboration to operationalize these ideas in practice.

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 aim to fill this gap by exploring 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. This emerging field -- legal alignment -- focuses on three research directions: (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 directions 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.