# Legal Alignment for Safe and Ethical AI

arXiv: https://arxiv.org/abs/2601.04175 · HTML: https://mlsanity.com/paper/2601.04175/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2026.

## Built on by (most cited first, 10 of 10)

- [The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems](https://mlsanity.com/paper/2602.17753/) (2026)
- [Positive Alignment: Artificial Intelligence for Human Flourishing](https://mlsanity.com/paper/2605.10310/) (2026)
- [Brainrot: Deskilling and Addiction are Overlooked AI Risks](https://mlsanity.com/paper/2605.03512/) (2026)
- [Commitment To Cooperation With Self-Negotiated Contracts](https://mlsanity.com/paper/2607.22750/) (2026)
- [Distributed Legal Infrastructure for a Trustworthy Agentic Web](https://mlsanity.com/paper/2603.06884/) (2026)
- [Algorithmic Authority and the Clinical Standard of Care](https://mlsanity.com/paper/2606.00044/) (2026)
- [The Agentic Web Requires New Normative Infrastructure](https://mlsanity.com/paper/2606.10711/) (2026)
- [Position: We Need Practical AI Alignment Methods to Mirror Human Reasoning](https://mlsanity.com/paper/2608.12372/) (2026)
- [Directing large language models to follow the letter or spirit of the law](https://mlsanity.com/paper/2609.23083/) (2026)
- [Better Call Reward: Reward Hacking as Strategic Abstention in Legal Reasoning Models](https://mlsanity.com/paper/2610.06439/) (2026)

## Abstract (arXiv)

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.