ArticleScientific reports2025
AI language model rivals expert ethicist in perceived moral expertise.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- ValuesML: A new multilingual dataset for values detection in news and political manifestos.Behavior research methods · 2026Article
- Users prefer generative AI to reflect real over idealized gender proportions.Scientific reports · 2026Article
- The AI interviewer: multi-faceted evaluation of adaptive questioning by large language models.Scientific reports · 2026Article
- Moral stereotyping in large language models.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Review
- A moral Turing test: How belief and source shape detection of and agreement with LLM judgments.PloS one · 2026Article
- Large language model attributes and perceived moral agency of artificial moral advisors.Frontiers in artificial intelligence · 2026Article
- Reply to: Stylistic language drives perceived moral superiority of LLMs.Scientific reports · 2025Article
- Pitfalls of large language models in medical ethics reasoning.NPJ digital medicine · 2025Article
- 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
- Can AI Rely on the Systematicity of Truth? The Challenge of Modelling Normative Domains.Philosophy & technology · 2025Article
- Robots in the moral loop: a field study of AI advisors in ethical military decision-making.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
People view AI as possessing expertise across various fields, but the perceived quality of AI-generated moral expertise remains uncertain. Recent work suggests that large language models (LLMs) perform well on tasks designed to assess moral alignment, reflecting moral judgments with relatively high accuracy. As LLMs are increasingly employed in decision-making roles, there is a growing expectation for them to offer not just aligned judgments but also demonstrate sound moral reasoning. Here, we advance work on the Moral Turing Test and find that Americans rate ethical advice from GPT-4o as slightly more moral, trustworthy, thoughtful, and correct than that of the popular New York Times advice column, The Ethicist. Participants perceived GPT models as surpassing both a representative sample of Americans and a renowned ethicist in delivering moral justifications and advice, suggesting that people may increasingly view LLM outputs as viable sources of moral expertise. This work suggests that people might see LLMs as valuable complements to human expertise in moral guidance and decision-making. It also underscores the importance of carefully programming ethical guidelines in LLMs, considering their potential to influence users' moral reasoning.
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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.