# Political Neutrality in AI Is Impossible- But Here Is How to Approximate It

arXiv: https://arxiv.org/abs/2503.05728 · HTML: https://mlsanity.com/paper/2503.05728/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2025.

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

- [SPARTA ALIGNMENT: Collectively Aligning Multiple Language Models through Combat](https://mlsanity.com/paper/2506.04721/) (2025)
- [Embracing Contradiction: Theoretical Inconsistency Will Not Impede the Road of Building Responsible AI Systems](https://mlsanity.com/paper/2505.18139/) (2025)
- [Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models](https://mlsanity.com/paper/2507.07484/) (2025)
- [On the Inevitability of Left-Leaning Political Bias in Aligned Language Models](https://mlsanity.com/paper/2507.15328/) (2025)
- [Epistemic diversity across language models mitigates knowledge collapse](https://mlsanity.com/paper/2512.15011/) (2025)
- [The Alignment Bottleneck](https://mlsanity.com/paper/2509.15932/) (2025)
- [When Do Language Models Endorse Limitations on Human Rights Principles?](https://mlsanity.com/paper/2603.04217/) (2026)
- [DeliberationBench: A Normative Benchmark for the Influence of Large Language Models on Users' Views](https://mlsanity.com/paper/2603.10018/) (2026)
- [Who Would You Vote For? Auditing Political Alignment in LLMs: An Italian Case-Study](https://mlsanity.com/paper/2608.11649/) (2026)
- [Issue Bias in Generative AI Writing Assistance: Political Issues and LLMs in the Swedish 2026 Election](https://mlsanity.com/paper/2609.15207/) (2026)

## Abstract (arXiv)

AI systems often exhibit political bias, influencing users' opinions and decisions. While political neutrality-defined as the absence of bias-is often seen as an ideal solution for fairness and safety, this position paper argues that true political neutrality is neither feasible nor universally desirable due to its subjective nature and the biases inherent in AI training data, algorithms, and user interactions. However, inspired by Joseph Raz's philosophical insight that "neutrality [...] can be a matter of degree" (Raz, 1986), we argue that striving for some neutrality remains essential for promoting balanced AI interactions and mitigating user manipulation. Therefore, we use the term "approximation" of political neutrality to shift the focus from unattainable absolutes to achievable, practical proxies. We propose eight techniques for approximating neutrality across three levels of conceptualizing AI, examining their trade-offs and implementation strategies. In addition, we explore two concrete applications of these approximations to illustrate their practicality. Finally, we assess our framework on current large language models (LLMs) at the output level, providing a demonstration of how it can be evaluated. This work seeks to advance nuanced discussions of political neutrality in AI and promote the development of responsible, aligned language models.