Legal Alignment for Safe and Ethical AI
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.
6964db64ba573026e23e3423
Noam Kolt
6964db64ba573026e23e3424
Nicholas Caputo
6964db64ba573026e23e3425
Jack Boeglin
6964db64ba573026e23e3426
Cullen O'Keefe
6964db64ba573026e23e3427
Rishi Bommasani
6964db64ba573026e23e3428
Stephen Casper
6964db64ba573026e23e3429
Mariano-Florentino Cuéllar
6964db64ba573026e23e342a
Noah Feldman
6964db64ba573026e23e342b
Iason Gabriel
6964db64ba573026e23e342c
Gillian K. Hadfield
6964db64ba573026e23e342d
Lewis Hammond
6964db64ba573026e23e342e
Peter Henderson
6964db64ba573026e23e342f
Atoosa Kasirzadeh
6964db64ba573026e23e3430
Seth Lazar
6964db64ba573026e23e3431
Anka Reuel
6964db64ba573026e23e3432
Kevin L. Wei
6964db64ba573026e23e3433
Jonathan Zittrain
Legal Alignment for Safe and Ethical AI
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.
Legal alignment explores how legal principles and methods can guide AI system design to ensure safety, ethics, and compliance through three research directions involving rule adherence, legal reasoning methods, and structural blueprints for AI reliability and trust.