AI language model rivals expert ethicist in perceived moral expertise
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 relativ...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-86510-0 |
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| _version_ | 1823862474676371456 |
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| author | Danica Dillion Debanjan Mondal Niket Tandon Kurt Gray |
| author_facet | Danica Dillion Debanjan Mondal Niket Tandon Kurt Gray |
| author_sort | Danica Dillion |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4d246ee29a324489b8d47d35d5f02442 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4d246ee29a324489b8d47d35d5f024422025-02-09T12:30:52ZengNature PortfolioScientific Reports2045-23222025-02-0115111510.1038/s41598-025-86510-0AI language model rivals expert ethicist in perceived moral expertiseDanica Dillion0Debanjan Mondal1Niket Tandon2Kurt Gray3Department of Psychology and Neuroscience, University of North Carolina at Chapel HillAllen Institute for Artificial IntelligenceAllen Institute for Artificial IntelligenceDepartment of Psychology and Neuroscience, University of North Carolina at Chapel HillAbstract 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.https://doi.org/10.1038/s41598-025-86510-0 |
| spellingShingle | Danica Dillion Debanjan Mondal Niket Tandon Kurt Gray AI language model rivals expert ethicist in perceived moral expertise Scientific Reports |
| title | AI language model rivals expert ethicist in perceived moral expertise |
| title_full | AI language model rivals expert ethicist in perceived moral expertise |
| title_fullStr | AI language model rivals expert ethicist in perceived moral expertise |
| title_full_unstemmed | AI language model rivals expert ethicist in perceived moral expertise |
| title_short | AI language model rivals expert ethicist in perceived moral expertise |
| title_sort | ai language model rivals expert ethicist in perceived moral expertise |
| url | https://doi.org/10.1038/s41598-025-86510-0 |
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