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.

翻译：人工智能（AI）的对齐既包含规范性问题——规定AI系统应如何行动，也包含技术性挑战——确保AI系统遵循这些规范。迄今为止，AI对齐研究普遍忽视了处理这些问题的重要知识来源与实践领域：法律。本文系统综述了旨在填补这一空白的"法律对齐"新兴领域，梳理了如何利用法律规则、原则及方法解决对齐问题，并指导设计安全、伦理运行的AI系统的相关研究。我们提出法律对齐三大核心研究路径的分类框架，探讨每条路径的实践操作化方式：(1)设计AI系统以遵循合法机构与程序制定的法律规则内容；(2)借鉴法律解释方法引导AI系统推理与决策；(3)将法律概念作为应对AI系统可靠性、信任与合作挑战的结构性蓝图。这些研究路径提出了新的概念、实证与制度性问题，包括明确特定AI系统应遵循的具体法律规范、构建评估其现实场景法律合规性的评测体系，以及设计支持法律对齐落地的治理框架。解决这些问题需要法律、计算机科学等多学科专业知识，为各领域学者协作设计更完善的AI系统提供了契机。