# When Do Language Models Endorse Limitations on Human Rights Principles?

arXiv: https://arxiv.org/abs/2603.04217 · HTML: https://mlsanity.com/paper/2603.04217/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Builds on (most cited first, 16 of 16)

- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://mlsanity.com/paper/2305.08283/) (2023)
- [Investigating Cultural Alignment of Large Language Models](https://mlsanity.com/paper/2402.13231/) (2024)
- [Measuring Political Bias in Large Language Models: What Is Said and How It Is Said](https://mlsanity.com/paper/2403.18932/) (2024)
- [Moral Foundations of Large Language Models](https://mlsanity.com/paper/2310.15337/) (2023)
- [Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs](https://mlsanity.com/paper/2503.08688/) (2025)
- [Language Model Alignment in Multilingual Trolley Problems](https://mlsanity.com/paper/2407.02273/) (2024)
- [Probing the Moral Development of Large Language Models through Defining Issues Test](https://mlsanity.com/paper/2309.13356/) (2023)
- [SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models](https://mlsanity.com/paper/2503.00137/) (2025)
- [Llama meets EU: Investigating the European Political Spectrum through the Lens of LLMs](https://mlsanity.com/paper/2403.13592/) (2024)
- [Political Neutrality in AI Is Impossible- But Here Is How to Approximate It](https://mlsanity.com/paper/2503.05728/) (2025)
- [Revisiting LLM Value Probing Strategies: Are They Robust and Expressive?](https://mlsanity.com/paper/2507.13490/) (2025)
- [Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models](https://mlsanity.com/paper/2506.12758/) (2025)
- [Rethinking AI Cultural Alignment](https://mlsanity.com/paper/2501.07751/) (2025)
- [Decoding Alignment: A Critical Survey of LLM Development Initiatives through Value-setting and Data-centric Lens](https://mlsanity.com/paper/2508.16982/) (2025)

## Built on by (most cited first, 1 of 1)

- [Toward Human Rights Benchmarking for LLMs: A Pilot Methodology](https://mlsanity.com/paper/2608.10268/) (2026)

## Abstract (arXiv)

As Large Language Models (LLMs) increasingly mediate global information access with the potential to shape public discourse, their alignment with universal human rights principles becomes important to ensure that these rights are abided by in high stakes AI-mediated interactions. In this paper, we evaluate how LLMs navigate trade-offs involving the Universal Declaration of Human Rights (UDHR), leveraging 1,152 synthetically generated scenarios across 24 rights articles and eight languages. Our analysis of eleven major LLMs reveals systematic biases where models: (1) accept limiting Economic, Social, and Cultural rights more often than Political and Civil rights, (2) demonstrate significant cross-linguistic variation with elevated endorsement rates of rights-limiting actions in Chinese and Hindi compared to English or Romanian, (3) show substantial susceptibility to prompt-based steering, and (4) exhibit noticeable differences between Likert and open-ended responses, highlighting critical challenges in LLM preference assessment.