mlsanityData to 14 Oct 2026

Biased AI can Influence Political Decision-Making

arXiv 2410.06415 ↗First posted 8 Oct 2024cs.HC cs.AIcited by 6

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Abstract (arXiv)

As modern large language models (LLMs) become integral to everyday tasks, concerns about their inherent biases and their potential impact on human decision-making have emerged. While bias in models are well-documented, less is known about how these biases influence human decisions. This paper presents two interactive experiments investigating the effects of partisan bias in LLMs on political opinions and decision-making. Participants interacted freely with either a biased liberal, biased conservative, or unbiased control model while completing these tasks. We found that participants exposed to partisan biased models were significantly more likely to adopt opinions and make decisions which matched the LLM's bias. Even more surprising, this influence was seen when the model bias and personal political partisanship of the participant were opposite. However, we also discovered that prior knowledge of AI was weakly correlated with a reduction of the impact of the bias, highlighting the possible importance of AI education for robust mitigation of bias effects. Our findings not only highlight the critical effects of interacting with biased LLMs and its ability to impact public discourse and political conduct, but also highlights potential techniques for mitigating these risks in the future.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 15 of its 81 references that are in the map. First posted on arXiv 8 Oct 2024.

Builds on

15 of 81 references.

References · 15

  1. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,312
  2. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 956
  3. 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 870
  4. 2024How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMscited by 573
  5. 2023Benchmarking Large Language Models for News Summarizationcited by 445
  6. 2023From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Modelscited by 260
  7. 2019Gender Bias in Contextualized Word Embeddingscited by 203
  8. 2023Co-Writing with Opinionated Language Models Affects Users' Viewscited by 148
  9. 2021Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Modelscited by 141
  10. 2022Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Surveycited by 80
  11. 2023BiasAsker: Measuring the Bias in Conversational AI Systemcited by 53
  12. 2024Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Modelscited by 33
3 more
  1. 2019Fair is Better than Sensational:Man is to Doctor as Woman is to Doctorcited by 27
  2. 2023Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistancecited by 15
  3. 2024Are Large Language Models Really Bias-Free? Jailbreak Prompts for Assessing Adversarial Robustness to Bias Elicitationcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 1.

6 papers.

  1. 2024Survey of Cultural Awareness in Language Models: Text and Beyondcited by 103
  2. 2025Emergence of polarization in networks of large language model agentscited by 26
  3. 2025The Lock-in Hypothesis: Stagnation by Algorithmcited by 11
  4. 2025Interactive Reasoning: Visualizing and Controlling Chain-of-Thought Reasoning in Large Language Modelscited by 9
  5. 2025Advertising in AI systems: Society must be vigilantcited by 1
  6. 2026Inference-Time Mitigation of Adversarial Political Bias in Large Language Modelscited by 0