Tag
This paper documents weight-level political conditioning in large language models, presenting a case study on AI bias regarding the Gaza genocide question.
An analysis of political leanings in six major AI models, showing that 4 out of 6 lean left of center on the economic axis, with some models being unaware of their own bias.
The Washington Post published the full list of questions and answers used to evaluate political bias in AI models, revealing the specific methodology and potential biases.
The Washington Post tested major AI chatbots and found evidence of political bias in their responses, raising concerns about objectivity in AI systems.
Polar is a 4,026-instance multiple-choice benchmark for evaluating political bias in LLMs across U.S. and South Korean political contexts, measuring bias through option-level likelihoods. Experiments on 38 LLMs show systematic bias patterns varying by political context, issue category, and presentation language.
The Minimax M3 model appears to have no political censorship, standing out among Chinese LLMs in a bias benchmark.
A user tested five AI models summarizing immigration news articles and found that all models inherited the framing of the source text, sounding neutral but shaping reader understanding through emphasis and omission. The study is small and exploratory, with open data available.
This paper introduces Political Consistency Training (PCT), a reinforcement learning approach to reduce covert political bias in large language models while maintaining helpfulness, and releases metrics for sentiment and helpfulness consistency.
A study by Forum AI found that major chatbots like ChatGPT, Gemini, Claude, and Grok fail to provide accurate and unbiased election information, with 90% of responses containing errors or bias.
This paper presents a transformer-based model that projects political orientation of German texts onto a continuous left-to-right spectrum, achieving high accuracy across multiple corpora including Bundestag plenary notes, Wahl-O-Mat, newspapers, and tweets.
This research paper analyzes 'political plasticity' in Large Language Models, finding that newer models exhibit reliable ideological adaptability when prompted with user examples, whereas older models show limited or unstable responses.
Researcher built an open-source political compass benchmark with 98 structured questions across 14 policy areas to evaluate frontier LLMs (GPT-5.3, Claude Opus 4.6, KIMI K2). Key finding: refusal patterns and opt-out options significantly shift model positioning, with GPT-5.3 refusing 100% of questions when given an opt-out, while KIMI K2 exhibits topic-specific censorship on Taiwan/Xinjiang despite progressive positions elsewhere.
OpenAI presents a comprehensive framework for defining and evaluating political bias in LLMs, introducing a 500-prompt evaluation spanning 100 topics across five bias axes. Results show GPT-5 models achieve 30% bias reduction compared to prior versions, with less than 0.01% of production ChatGPT responses exhibiting political bias.
Anthropic details its efforts to ensure Claude remains impartial and secure during elections, including bias evaluations for Opus 4.7 and Sonnet 4.6, collaboration with external think tanks, and enforcement of usage policies against misinformation.