Disclosure of AI-Generated News Increases Engagement but Does Not Reduce Aversion, Despite Positive Quality Ratings
<|reference_start|>Disclosure of AI-Generated News Increases Engagement but Does Not Reduce Aversion, Despite Positive Quality Ratings: The advancement of artificial intelligence (AI) has led to its application in many areas, including journalism. One key issue is the public's perception of AI-generated content. This preregistered study investigates (i) the perceived quality of AI-assisted and AI-generated versus human-generated news articles, (ii) whether disclosure of AI's involvement in generating these news articles influences engagement with them, and (iii) whether such awareness affects the willingness to read AI-generated articles in the future. We employed a between-subjects survey experiment with 599 participants from the German-speaking part of Switzerland, who evaluated the credibility, readability, and expertise of news articles. These articles were either written by journalists (control group), rewritten by AI (AI-assisted group), or entirely generated by AI (AI-generated group). Our results indicate that all news articles, regardless of whether they were written by journalists or AI, were perceived to be of equal quality. When participants in the treatment groups were subsequently made aware of AI's involvement in generating the articles, they expressed a higher willingness to engage with (i.e., continue reading) the articles than participants in the control group. However, they were not more willing to read AI-generated news in the future. These results suggest that aversion to AI usage in news media is not primarily rooted in a perceived lack of quality, and that by disclosing using AI, journalists could attract more immediate engagement with their content, at least in the short term.<|reference_end|>
@article{gilardi2024disclosure,
    title={Disclosure of AI-Generated News Increases Engagement but Does Not Reduce
  Aversion, Despite Positive Quality Ratings},
    author={Fabrizio Gilardi, Sabrina Di Lorenzo, Juri Ezzaini, Beryl Santa,
  Benjamin Streiff, Eric Zurfluh, Emma Hoes},
    journal={arXiv preprint arXiv:2409.03500},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03500},
    primaryClass={cs.CY cs.AI}
}