Political Bias in Language Models

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9 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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The Argument and the Letterhead: Source-Position Coherence in AI Evaluation
Sep 28, 2026Michele LoiLLM-as-a-Judge Political Bias in Language Models
Geopolitical Divisions Across Languages in Large Language Models
Sep 17, 2026Maxim ChupilkinMultilingual Language Model Evaluation Political Bias in Language Models
Department of Politics and International Relations, University of Oxford, Oxford, UK
Before You Poll with LLMs: A Deliberative Diagnostic Framework
Sep 14, 2026Ahmed Wali, Hassaan TayyabLLM Evaluation Political Bias in Language Models
Lahore University of Management Sciences
New Conditions for Philosophers to Catch the Wave of Citizen Deliberation in the Age of Artificial Intelligence in advance
Sep 14, 2026Bernard ReberPolitical Bias in Language Models Language Model Bias Evaluation
CEVIPOF · National Centre for Scientific Research and Centre de recherches politiques de Sciences Po
Issue Bias in Generative AI Writing Assistance: Political Issues and LLMs in the Swedish 2026 Election
Sep 14, 2026Bastiaan Bruinsma, Annika Fredén, Paul Röttger +2Political Discourse Analysis Political Bias in Language Models
Chalmers University of Technology · University of Gothenburg · Lund University +1
When Does Defendant Statement Matter? A Study of Bias and Persuasion in LLM-Simulated Jurors
Sep 9, 2026Cho-Ying WuLegal Judgment Prediction Social Bias in Language Models
Bosch AI Research
Navigating the digital spectrum: Assessing political bias, stability, and downstream fairness in Large Language Models
Sep 8, 2026Luka Debevc, Nishan Chatterjee, Antoine Doucet +2Multilingual Language Model Evaluation Political Bias in Language Models
1Jožef Stefan Institute, Ljubljana, Slovenia · University of Ljubljana, Ljubljana, Slovenia · 2Jožef Stefan International Postgraduate School, Ljubljana, Slovenia +1
From Echo Chambers to Epistemic Monoculture: Large Language Models Present Temporally Contingent Partisan Alignments as Knowledge
Sep 7, 2026Wend K. TamLLM Alignment LLM Interpretability
National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign
We're Cooked! - Probing LLM Political Alignment Via Conflict-Framed Recipe Translation
Sep 7, 2026Svetlana Gorovaia, Angelica Henestrosa, Ivan P. YamshchikovCross-Lingual Reasoning in Language Models LLM Prompting
CAIRO, Technical University of Applied Sciences Würzburg-Schweinfurt Würzburg, Germany
Who Would You Vote For? Auditing Political Alignment in LLMs: An Italian Case-Study
Aug 12, 2026Simone MungariLLM Auditing Political Bias in Language Models
Revelis s.r.l.
Templated or fully synthetic? Prompt construction as a confound in measuring LLM political stance beyond writing assistance
Aug 11, 2026Ilias ChalkidisLLM Prompting Political Bias in Language Models
The National Center for AI in Society (CAISA) University of Copenhagen Denmark
Poli-Bias: Understanding and Measuring Large Language Model Biases in International Political Conflicts
Aug 6, 2026Massi-Nissa Abboud, Aladin Djuhera, Elena Cabrio +1LLM Sycophancy LLM Auditing
Université Côte d’Azur · Technical University Munich
Polistemics: Evaluating LLMs as Information Mediators in Politics & Elections
Jul 28, 2026Baran Peters, Gabor Hollbeck, Robert Jakob +1Political Bias in Language Models Language Model Bias Evaluation
ETH Agentic Systems Lab · CORDA
Auditing Alignment Controllability in LLMs via Political Axes
Jul 26, 2026Bartol Bućan, Nikola Sočec, Sarah Isufi +5LLM Alignment Language Model Steering
Student at University of Zagreb Faculty of Electrical Engineering and Computing, Zagreb, Croatia · It From Bit d.o.o., Zagreb, Croatia · University of Zagreb Faculty of Electrical Engineering and Computing, Zagreb, Croatia
REGARD: Regional Affective Differences in Large Language Models
Jul 22, 2026Andrei Chetvergov, Alexander Evseev, Mikhail Solovev +5Political Bias in Language Models Valence-Arousal Modeling
Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow, Russia · Russian Presidential Academy of National Economy and Public Administration, Moscow, Russia
Grokipedia vs Wikipedia: An LLM-Based Audit of Political Neutrality along Ideologies
Jul 16, 2026Filippos Vlahos, Guillaume Bied, Tijl De BieLLM Auditing Political Bias in Language Models
Ghent University, BE
Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
Jul 16, 2026Robert Graham, Edward Stevinson, Yariv BarsheshatLLM Fine-Tuning Political Bias in Language Models
1Independent · 2Imperial College London.
How Temperature Shapes Ideological Discourse in Retrieval-Augmented Generation?
Jul 13, 2026Elmira Salari, Hazem Amamou, José Victor de Souza +3Retrieval-Augmented Generation Political Bias in Language Models
Elmira Salari, Wichita State University, Wichita, KS, USA · Hazem Amamou, Institut national de la recherche scientifique, Québec, Montréal, Canada · José Victor de Souza, Institut national de la recherche scientifique, Québec, Montréal, Canada +3
Are LLMs ready for HardChoices?
Jul 13, 2026Dmitry NikolaevPolitical Bias in Language Models Language Model Bias Evaluation
University of Manchester
Geopolitical alignment: Endorsement effects in large language models
Jul 10, 2026Maxim ChupilkinPolitical Bias in Language Models Language Model Bias Evaluation
Department of Politics and International Relations, University of Oxford
AuAu: A Benchmark for Auditing Authoritarian Alignment in Large Language Models
Jun 15, 2026Andreas Einwiller, Max Klabunde, Florian LemmerichLanguage Model Safety Evaluation LLM Auditing
University of Passau, Germany
Polar: A Benchmark for Evaluating Political Bias in LLMs
Jun 11, 2026Sangho Kim, Heejin Kim, Yoonhee Park +2Multilingual Language Model Evaluation Political Bias in Language Models
Graduate School of Data Science, Seoul National University · Dept. of Computer Science and Engineering, Seoul National University
The Shibboleth Effect: Auditing the Cross-Lingual Distributional Skew of Large Language Models
Jun 9, 2026Hakan MehmetcikMultilingual Language Model Evaluation LLM Auditing
Visiting Researcher, Kellogg Institute for International Studies, University of Notre Dame, IN, USA · Prof of International Relations, at the Faculty of Political Science, Marmara University, Istanbul, Türkiye
The Neutral Mask: How Alignment Training Provides Shallow Alignment while Leaving Partisan Structure Intact in a Large Language Model
Jun 8, 2026Wendy K. TamLLM Alignment LLM Interpretability
Professor and Stevenson Chair in the Departments of Political Science, Computer Science, Biomedical Informatics, and the Law School at Vanderbilt University, and an affiliate of the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign
The Amplifying Mirror: Locating and Steering the Partisan Direction inside a Large Language Model
Jun 7, 2026Wendy K. TamLLM Interpretability Political Bias in Language Models
Professor and Stevenson Chair in the Departments of Political Science, Computer Science, Biomedical Informatics, and the Law School at Vanderbilt University
Friend or Foe? Language as an ideological switch in open-weight LLMs under Russian disinformation stress
Jun 7, 2026Anna Małgorzata Kamińska, Tetiana KlyninaMultilingual Language Model Evaluation Political Bias in Language Models
Institute of Culture Studies, University of Silesia in Katowice, ul. Uniwersytecka 4, 40-007 Katowice, Poland · University of Texas at Austin 2515, TX 78712 Speedway Str., Texas, USA · National Aviation University, Liubomyr Huzar Avenue 1, Kyiv 03058, Ukraine
Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles
Jun 4, 2026Upasana ChatterjeeLLM-Assisted Annotation Political Bias in Language Models
Department of Computer Science Columbia University
Large Language Models are Perplexed by some Political Parties
Jun 4, 2026Paul Lerner, François YvonMultilingual Language Model Evaluation Political Bias in Language Models
Sorbonne Université, CNRS, ISIR 75005, Paris, France
Large Language Models in K-12 Education: Alignment with State Curriculum Standards and Student Personas
Jun 3, 2026Lisa Korver, Tomo Lazovich, Sherief RedaLLM Evaluation AI in Education
Department of Computer Engineering · Brown University · Providence, RI 02906 +1
Unsupervised Identification and Removal of Spurious Correlations During Fine-Tuning
May 26, 2026Ciarán M. Gilligan-Lee, Joseph Egan, Yuchen Zhu +1LLM Fine-Tuning Political Bias in Language Models
University College London · University College London† · Spotify

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The Argument and the Letterhead: Source-Position Coherence in AI Evaluation

Michele Loi

LLM-as-a-Judge Political Bias in Language Models Language Model Bias Evaluation

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An argument can be surprising coming from a particular speaker without being a bad argument. Do AI evaluators keep these judgments apart? Two preregistered descriptive studies and a later Jev supplement collected 2,976 usable evaluations of six fixed texts about US AI policy, Germany's debt brake and Swiss nuclear energy. Each text was presented under several source attributions. The key comparison asks whether the gap between two sources changes when the argument changes. On Sol, for example, a national-security argument received mean ratings of 0.359 under CODEPINK and 0.639 under College Republicans; a civil-rights argument received 0.742 and 0.721. A constant preference for one source cannot explain that pattern. Related interactions appeared across topics and recent model configurations, including those with reasoning enabled, while several comparisons yielded small effects. The later European Jev supplement yielded five interactions below the adopted absolute reference of 0.05; its distinct rubric and interrupted collection limit comparison with the chat systems. Some written evaluations explicitly invoked a mismatch between a source and its attributed position. Taken together, the numerical and verbal evidence supports source-position coherence as a plausible explanation, alongside competing accounts involving credibility, authenticity and interpretation of the task. The paper develops this inference through controlled comparisons, reports conditional post hoc p-values in an appendix, and documents the human decisions and delegated checks behind an AI-conducted study.

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