ArticleProceedings of the National Academy of Sciences of the United States of America2025
Large language models show amplified cognitive biases in moral decision-making.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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Who cites it
15 citing papers in PubMed.
- The 5R's of large language model-assisted diagnosis: A practical framework for hospitalists.Journal of hospital medicine · 2026Article
- AI cannot replace human participants in behavioral research.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Disentangling interaction and bias effects in opinion dynamics of large language models.Nature communications · 2026Article
- Implementing a Cognitively Grounded Artificial Moral Advisor: A Multi-LLM Multi-Agent Approach Based on the Cognitive-Reflective Equilibration Model.Journal of Intelligence · 2026Article
- Article
- Algorithmic bias in clinical resource allocation by a large language model: a cross-sectional in-silico evaluation of 13,608 decisions.BMC medical ethics · 2026Article
- The AI interviewer: multi-faceted evaluation of adaptive questioning by large language models.Scientific reports · 2026Article
- LLMs displaying less cognitive bias are not necessarily better decision makers.Nature machine intelligence · 2026Article
- The Decision Process Scale (DPS): Self-report measures of reliance on rules, cost-benefit reasoning, intuition, and deliberation in (moral) decision-making.Behavior research methods · 2026Article
- Article
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- Large language models show amplified cognitive biases in moral decision-making.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Comparing AI and human moral reasoning: context-sensitive patterns beyond utilitarian bias.Frontiers in artificial intelligence · 2025Article
- The strengths, weaknesses, opportunities, and threats of generative artificial intelligence: a qualitative study of undergraduate nursing students.Frontiers in public health · 2025Article
- Opportunities and challenges of using generative AI to personalize educational assessment.Frontiers in artificial intelligence · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
As large language models (LLMs) become more widely used, people increasingly rely on them to make or advise on moral decisions. Some researchers even propose using LLMs as participants in psychology experiments. It is, therefore, important to understand how well LLMs make moral decisions and how they compare to humans. We investigated these questions by asking a range of LLMs to emulate or advise on people's decisions in realistic moral dilemmas. In Study 1, we compared LLM responses to those of a representative U.S. sample (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.