生成式 AI 写作辅助中的议题偏见：瑞典 2026 年选举中的政治议题与大型语言模型

Computer Science > Artificial Intelligence arXiv:2609.15207 (cs) [Submitted on 14 Sep 2026] Title:Issue Bias in Generative AI Writing Assistance: Political Issues and LLMs in the Swedish 2026 Election Authors:Bastiaan Bruinsma, Annika Fredén, Paul Röttger, Moa Johansson, Asad Sayeed View a PDF of the paper titled Issue Bias in Generative AI Writing Assistance: Political Issues and LLMs in the Swedish 2026 Election, by Bastiaan Bruinsma and 4 other authors View PDF HTML (experimental) 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. Subjects: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Applications (stat.AP) Cite as: 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) Submission history From: Bastiaan Bruinsma [view email] [v1] Mon, 14 Sep 2026 08:28:14 UTC (65 KB) Full-text links: Access Paper: View a PDF of the paper titled Issue Bias in Generative AI Writing Assistance: Political Issues and LLMs in the Swedish 2026 Election, by Bastiaan Bruinsma and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2026-09 Change to browse by: cs cs.CY stat stat.AP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)