mlsanityData to 7 Oct 2026

A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germany

arXiv 2502.15568 ↗First posted 21 Feb 2025cs.LG cs.AIcited by 5

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Abstract (arXiv)

In this study, we examine the reliability of AI-based Voting Advice Applications (VAAs) and large language models (LLMs) in providing objective political information. Our analysis is based upon a comparison with party responses to 38 statements of the Wahl-O-Mat, a well-established German online tool that helps inform voters by comparing their views with political party positions. For the LLMs, we identify significant biases. They exhibit a strong alignment (over 75% on average) with left-wing parties and a substantially lower alignment with center-right (smaller 50%) and right-wing parties (around 30%). Furthermore, for the VAAs, intended to objectively inform voters, we found substantial deviations from the parties' stated positions in Wahl-O-Mat: While one VAA deviated in 25% of cases, another VAA showed deviations in more than 50% of cases. For the latter, we even observed that simple prompt injections led to severe hallucinations, including false claims such as non-existent connections between political parties and right-wing extremist ties.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 21 Feb 2025.

Builds on

6 of 28 references.

References · 6

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  3. 2023The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientationcited by 183
  4. 2024Measuring Political Bias in Large Language Models: What Is Said and How It Is Saidcited by 89
  5. 2023The Self-Perception and Political Biases of ChatGPTcited by 66
  6. 2024Assessing Political Bias in Large Language Modelscited by 32

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.