# Rules, Cases, and Reasoning: Positivist Legal Theory as a Framework for Pluralistic AI Alignment

arXiv: https://arxiv.org/abs/2410.17271 · HTML: https://mlsanity.com/paper/2410.17271/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 49 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 21 of 21)

- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [A General Language Assistant as a Laboratory for Alignment](https://mlsanity.com/paper/2112.00861/) (2021)
- [Discovering Language Model Behaviors with Model-Written Evaluations](https://mlsanity.com/paper/2212.09251/) (2022)
- [Aligning AI With Shared Human Values](https://mlsanity.com/paper/2008.02275/) (2020)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [AI safety via debate](https://mlsanity.com/paper/1805.00899/) (2018)
- [Pretraining Language Models with Human Preferences](https://mlsanity.com/paper/2302.08582/) (2023)
- [Emergent Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2212.09196/) (2022)
- [Measuring Progress on Scalable Oversight for Large Language Models](https://mlsanity.com/paper/2211.03540/) (2022)
- [The Capacity for Moral Self-Correction in Large Language Models](https://mlsanity.com/paper/2302.07459/) (2023)
- [Alignment of Language Agents](https://mlsanity.com/paper/2103.14659/) (2021)
- [The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models](https://mlsanity.com/paper/2404.16019/) (2024)
- [Jury Learning: Integrating Dissenting Voices into Machine Learning Models](https://mlsanity.com/paper/2202.02950/) (2022)
- [Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties](https://mlsanity.com/paper/2309.00779/) (2023)
- [When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Tasks](https://mlsanity.com/paper/2305.06626/) (2023)
- [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://mlsanity.com/paper/2303.05453/) (2023)
- [Training Language Models with Language Feedback](https://mlsanity.com/paper/2204.14146/) (2022)
- [From Pseudorandomness to Multi-Group Fairness and Back](https://mlsanity.com/paper/2301.08837/) (2023)
- [Case Repositories: Towards Case-Based Reasoning for AI Alignment](https://mlsanity.com/paper/2311.10934/) (2023)
- [Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarios](https://mlsanity.com/paper/2407.19760/) (2024)

## Built on by (most cited first, 2 of 2)

- [Decoding Alignment: A Critical Survey of LLM Development Initiatives through Value-setting and Data-centric Lens](https://mlsanity.com/paper/2508.16982/) (2025)
- [Overton Pluralistic Reinforcement Learning for Large Language Models](https://mlsanity.com/paper/2602.20759/) (2026)

## Abstract (arXiv)

Legal theory can address two related key problems of alignment: pluralism and specification. Alignment researchers must determine how to specify what is concretely meant by vague principles like helpfulness and fairness and they must ensure that their techniques do not exclude alternative perspectives on life and values. The law faces these same problems. Leading legal theories suggest the law solves these problems through the interaction of rules and cases, where general rules promulgated by a democratic authority are given specific content through their application over time. Concrete applications allow for convergence on practical meaning while preserving space for disagreement on values. These approaches suggest improvements to existing democratic alignment processes that use AI to create cases that give content to rules, allowing for more pluralist alignment.