ArticleFrontiers in artificial intelligence2024
The impact of text topic and assumed human vs. AI authorship on competence and quality assessment.
Article in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A moral Turing test: How belief and source shape detection of and agreement with LLM judgments.PloS one · 2026Article
- AI-Assisted Hypothesis Generation to Address Challenges in Cardiotoxicity Research: Simulation Study Using ChatGPT With GPT-4o.Journal of medical Internet research · 2025Article
- Understanding and Perception of Automated Text Generation among the Public: Two Surveys with Representative Samples in Germany.Behavioral sciences (Basel, Switzerland) · 2024Article
- The impact of AI on education and careers: What do students think?Frontiers in artificial intelligence · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Background: While Large Language Models (LLMs) are considered positively with respect to technological progress and abilities, people are rather opposed to machines making moral decisions. But the circumstances under which algorithm aversion or algorithm appreciation are more likely to occur with respect to LLMs have not yet been sufficiently investigated. Therefore, the aim of this study was to investigate how texts with moral or technological topics, allegedly written either by a human author or by ChatGPT, are perceived. Methods: In a randomized controlled experiment, Results: We only found a small interaction effect for perceived author competence, Conclusion: These results are more in line with previous findings on algorithm aversion than with algorithm appreciation. We discuss the implications of these findings for the acceptance of the use of LLMs for text composition.
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Registered trials
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