ArticleProceedings of the National Academy of Sciences of the United States of America2023
Large language models show human-like content biases in transmission chain experiments.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluating and addressing demographic disparities in medical large language models: a systematic review.International journal for equity in health · 2025Pooled it
- Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review.JAMA · 2025Pooled it
- Towards scalable psychological first aid training: an autoethnographic exploration of the potential of large language models in simulation-based learning.European journal of psychotraumatology · 2026Article
- Propagation and preservation of AI-discovered problem-solving strategies in human culture.Nature communications · 2026Article
- Article
- Large language models perpetuate bias in palliative care: Development and analysis of the Palliative Care Adversarial Dataset (PCAD).PLOS digital health · 2026Article
- Evaluating Elicit's systematic reviews workflow in an umbrella review on air pollution and acute lower respiratory infections: a methodological study for quality appraisal.BMC medical research methodology · 2026Article
- Same Prompt, Different Answer: Why AI Disagrees with Itself.Journal of general internal medicine · 2026Article
- Age and generational differences in anthropomorphism and trust in large language models.Frontiers in cognition · 2026Article
- Regulating genome language models: navigating policy challenges at the intersection of AI and genetics.Human genetics · 2025Review
- Challenges of Implementing LLMs in Clinical Practice: Perspectives.Journal of clinical medicine · 2025Article
- Figure plagiarism and manipulation, an under-recognised problem in academia.European radiology · 2025Review
- Norms emerge through iterated learning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Regulating the AI-enabled ecosystem for human therapeutics.Communications medicine · 2025Review
- Machine Learning Models With Prognostic Implications for Predicting Gastrointestinal Bleeding After Coronary Artery Bypass Grafting and Guiding Personalized Medicine: Multicenter Cohort Study.Journal of medical Internet research · 2025Article
- Assessing and alleviating state anxiety in large language models.NPJ digital medicine · 2025Article
- Signalling and social learning in swarms of robots.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Current applications and challenges in large language models for patient care: a systematic review.Communications medicine · 2025Article
- Trade-offs, control conditions, and alternative designs in the experimental study of cultural evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024Review
- Wisdom of the silicon crowd: LLM ensemble prediction capabilities rival human crowd accuracy.Science advances · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the use of large language models (LLMs) grows, it is important to examine whether they exhibit biases in their output. Research in cultural evolution, using transmission chain experiments, demonstrates that humans have biases to attend to, remember, and transmit some types of content over others. Here, in five preregistered experiments using material from previous studies with human participants, we use the same, transmission chain-like methodology, and find that the LLM ChatGPT-3 shows biases analogous to humans for content that is gender-stereotype-consistent, social, negative, threat-related, and biologically counterintuitive, over other content. The presence of these biases in LLM output suggests that such content is widespread in its training data and could have consequential downstream effects, by magnifying preexisting human tendencies for cognitively appealing and not necessarily informative, or valuable, content.
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Registered trials
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