ArticleScientific reports2024
Attributions toward artificial agents in a modified Moral Turing Test.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Advice quality and source disclosure shape trust in AI-generated ethical advice.Scientific reports · 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
- Stylistic language drives perceived moral superiority of LLMs.Scientific reports · 2025Article
- Reply to: Stylistic language drives perceived moral superiority of LLMs.Scientific reports · 2025Article
- Augmenting intensive care unit nursing practice with generative AI: A formative study of diagnostic synergies using simulation-based clinical cases.Journal of clinical nursing · 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
- AI language model rivals expert ethicist in perceived moral expertise.Scientific reports · 2025Article
- Robots in the moral loop: a field study of AI advisors in ethical military decision-making.Frontiers in artificial intelligence · 2025Article
- Disagreements in Medical Ethics Question Answering Between Large Language Models and Physicians.Research square · 2024Article
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9 authors.
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Abstract
Advances in artificial intelligence (AI) raise important questions about whether people view moral evaluations by AI systems similarly to human-generated moral evaluations. We conducted a modified Moral Turing Test (m-MTT), inspired by Allen et al. (Exp Theor Artif Intell 352:24-28, 2004) proposal, by asking people to distinguish real human moral evaluations from those made by a popular advanced AI language model: GPT-4. A representative sample of 299 U.S. adults first rated the quality of moral evaluations when blinded to their source. Remarkably, they rated the AI's moral reasoning as superior in quality to humans' along almost all dimensions, including virtuousness, intelligence, and trustworthiness, consistent with passing what Allen and colleagues call the comparative MTT. Next, when tasked with identifying the source of each evaluation (human or computer), people performed significantly above chance levels. Although the AI did not pass this test, this was not because of its inferior moral reasoning but, potentially, its perceived superiority, among other possible explanations. The emergence of language models capable of producing moral responses perceived as superior in quality to humans' raises concerns that people may uncritically accept potentially harmful moral guidance from AI. This possibility highlights the need for safeguards around generative language models in matters of morality.
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