Users Favor LLM-Generated Content -- Until They Know It's AI
Not on the map. Why?
Abstract (arXiv)
In this paper, we investigate how individuals evaluate human and large langue models generated responses to popular questions when the source of the content is either concealed or disclosed. Through a controlled field experiment, participants were presented with a set of questions, each accompanied by a response generated by either a human or an AI. In a randomized design, half of the participants were informed of the response's origin while the other half remained unaware. Our findings indicate that, overall, participants tend to prefer AI-generated responses. However, when the AI origin is revealed, this preference diminishes significantly, suggesting that evaluative judgments are influenced by the disclosure of the response's provenance rather than solely by its quality. These results underscore a bias against AI-generated content, highlighting the societal challenge of improving the perception of AI work in contexts where quality assessments should be paramount.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 4 of its 22 references that are in the map. First posted on arXiv 23 Feb 2025.
Builds on
4 of 22 references.
References · 4
Built on by
Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025Neither Valid nor Reliable? Investigating the Use of LLMs as Judgescited by 37
- 2025Large Language Model Sourcing: A Surveycited by 5
- 2026Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agentscited by 4
- 2026LLM or Human? Perceptions of Trust and Information Quality in Research Summariescited by 0
- 2026Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents1 Feb 2026
- 2026LLM or Human? Perceptions of Trust and Information Quality in Research Summaries22 Jan 2026
- 2025Large Language Model Sourcing: A Survey11 Oct 2025
- 2025Neither Valid nor Reliable? Investigating the Use of LLMs as Judges25 Aug 2025