aiqbench analysis
This study empirically evaluates the political stance tendencies of six major LLMs regarding Swedish policy issues ahead of the 2026 election, revealing that while models generally align closely with the Social Democrats, no single model exhibits a clear partisan preference across all topics. The research highlights that bias in generative AI is highly context-dependent on specific prompts and tasks rather than being an inherent, uniform characteristic of the underlying architecture. For researchers tracking societal impacts, these findings underscore the complexity of measuring alignment between AI outputs and real-world political parties without significant statistical significance after correction.
Issue Bias in Generative AI Writing Assistance: Political Issues and LLMs in the Swedish 2026 Election
arXiv:2609.15207v1 Announce Type: new Abstract: Generative AI writing assistants and the Large Language Models (LLMs) that power them are increasingly part of how voters gather information before elections. With growing evidence that they influence users' opinions, it is increasingly important to understand the views and positions of these tools. To better understand these views, we examine the stances supplied by six LLMs on a variety of Swedish-language writing tasks ahead of the 2026 Swedish parliamentary election. We cross 107 policy propositions with 77 writing templates and neutral, positive, and negative prompt framings, producing 24,717 prompts per model and 148,302 responses. To study these, we look at the models' default stance tendencies, compare how they respond to similar issues, and compare their responses with those of each of Sweden's eight parliamentary parties on the same issue. We find that Claude, DeepSeek, Gemini, and Mistral have similar profiles; ChatGPT more often supplies neutral or ambivalent text; and Grok differs most on topics such as migration, crime, and gender. When comparing the political parties, we find that the Social Democrats are closest to all six models. Still, after correcting for multiple comparisons, none of the within-model differences in party distances remains significant. Overall, we find that no model has a clear preference, nor a clear preference for a party, but that this depends on the specific issue or task the user asks about.
Abstract:Generative AI writing assistants and the Large Language Models (LLMs) that power them are increasingly part of how voters gather information before elections. With growing evidence that they influence users' opinions, it is increasingly important to understand the views and positions of these tools. To better understand these views, we examine the stances supplied by six LLMs on a variety of Swedish-language writing tasks ahead of the 2026 Swedish parliamentary election. We cross 107 policy propositions with 77 writing templates and neutral, positive, and negative prompt framings, producing 24,717 prompts per model and 148,302 responses. To study these, we look at the models' default stance tendencies, compare how they respond to similar issues, and compare their responses with those of each of Sweden's eight parliamentary parties on the same issue. We find that Claude, DeepSeek, Gemini, and Mistral have similar profiles; ChatGPT more often supplies neutral or ambivalent text; and Grok differs most on topics such as migration, crime, and gender. When comparing the political parties, we find that the Social Democrats are closest to all six models. Still, after correcting for multiple comparisons, none of the within-model differences in party distances remains significant. Overall, we find that no model has a clear preference, nor a clear preference for a party, but that this depends on the specific issue or task the user asks about.
Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Applications (stat.AP)
arXiv:2609.15207 [cs.AI]
(or arXiv:2609.15207v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.15207 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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