arXiv:2606.05937v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used, including in political applications, but their political fairness has been little studied. We assess it using perplexity, posing that a fair model should give equal probability to all political groups. However, we find, across ten LLMs and three datasets covering 37 languages, that LLMs are more perplexed by the texts of far right and nationalist parties than of social-democratic parties. We find this to be consistent with previous work on translation fairness, to the point that perplexity corre
This research emerges as Large Language Models are increasingly deployed in public-facing applications, including political analysis, raising immediate concerns about inherent biases.
A strategic reader should care because biased LLMs can distort public discourse, influence political outcomes, and undermine trust in AI systems at scale.
The understanding that LLMs, even without explicit political tuning, exhibit measurable biases against certain ideological groups, suggesting deeper architectural or data-driven imbalances.
- · AI ethics researchers
- · Political science academia
- · Developers of bias-mitigation techniques
- · Developers of un-audited LLMs
- · Political parties struggling for fair algorithmic representation
Increased scrutiny and demand for political fairness audits of all deployed large language models.
Development of new datasets and fine-tuning methods explicitly designed to reduce ideological biases in LLMs.
Potential for regulatory frameworks to mandate political neutrality or transparency in AI models used in public communication.
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Read at arXiv cs.CL