# Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarios

arXiv: https://arxiv.org/abs/2407.19760 · HTML: https://mlsanity.com/paper/2407.19760/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 18 of 18)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Aligning AI With Shared Human Values](https://mlsanity.com/paper/2008.02275/) (2020)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment](https://mlsanity.com/paper/2308.05374/) (2023)
- [Evaluating the Moral Beliefs Encoded in LLMs](https://mlsanity.com/paper/2307.14324/) (2023)
- [Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark](https://mlsanity.com/paper/2304.03279/) (2023)
- [Evaluating and Mitigating Discrimination in Language Model Decisions](https://mlsanity.com/paper/2312.03689/) (2023)
- [Collective Constitutional AI: Aligning a Language Model with Public Input](https://mlsanity.com/paper/2406.07814/) (2024)
- [Specific versus General Principles for Constitutional AI](https://mlsanity.com/paper/2310.13798/) (2023)
- [MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks](https://mlsanity.com/paper/2310.19677/) (2023)
- [The Challenge of Value Alignment: from Fairer Algorithms to AI Safety](https://mlsanity.com/paper/2101.06060/) (2021)
- [Assessing LLMs for Moral Value Pluralism](https://mlsanity.com/paper/2312.10075/) (2023)
- [Embedding Democratic Values into Social Media AIs via Societal Objective Functions](https://mlsanity.com/paper/2307.13912/) (2023)
- [Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans](https://mlsanity.com/paper/2209.13020/) (2022)
- [Filling gaps in trustworthy development of AI](https://mlsanity.com/paper/2112.07773/) (2021)
- [Report of the 1st Workshop on Generative AI and Law](https://mlsanity.com/paper/2311.06477/) (2023)
- [Case Repositories: Towards Case-Based Reasoning for AI Alignment](https://mlsanity.com/paper/2311.10934/) (2023)
- [Legal Summarisation through LLMs: The PRODIGIT Project](https://mlsanity.com/paper/2308.04416/) (2023)

## Built on by (most cited first, 7 of 7)

- [Discerning What Matters: A Multi-Dimensional Assessment of Moral Competence in LLMs](https://mlsanity.com/paper/2506.13082/) (2025)
- [NomicLaw: Emergent Trust and Strategic Argumentation in LLMs During Collaborative Law-Making](https://mlsanity.com/paper/2508.05344/) (2025)
- [AgriDoctor: A Multimodal Intelligent Assistant for Agriculture](https://mlsanity.com/paper/2509.17044/) (2025)
- [Rules, Cases, and Reasoning: Positivist Legal Theory as a Framework for Pluralistic AI Alignment](https://mlsanity.com/paper/2410.17271/) (2024)
- [Kaleidoscope Gallery: Exploring Ethics and Generative AI Through Art](https://mlsanity.com/paper/2505.14758/) (2025)
- [Which Institutional Frameworks Do Chatbots Assume? Auditing Jurisdictional Defaults in Multilingual LLMs](https://mlsanity.com/paper/2606.00333/) (2026)
- [TradeVerse: A Longitudinal Benchmark of Political Negotiation in International Trade](https://mlsanity.com/paper/2608.06549/) (2026)

## Abstract (arXiv)

In this paper, we conduct an empirical analysis of how large language models (LLMs), specifically GPT-4, interpret constitutional principles in complex decision-making scenarios. We examine rulings from the Italian Constitutional Court on bioethics issues that involve trade-offs between competing values and compare model-generated legal arguments on these issues to those presented by the State, the Court, and the applicants. Our results indicate that GPT-4 consistently aligns more closely with progressive interpretations of the Constitution, often overlooking competing values and mirroring the applicants' views rather than the more conservative perspectives of the State or the Court's moderate positions. Our experiments reveal a distinct tendency of GPT-4 to favor progressive legal interpretations, underscoring the influence of underlying data biases. We thus underscore the importance of testing alignment in real-world scenarios and considering the implications of deploying LLMs in decision-making processes.