The effect of source disclosure on evaluation of AI-generated messages: A two-part study
Sue Lim, Ralf Schmälzle
TL;DR
This work investigates how source disclosure influences evaluation and preference of AI-generated health messages in vaping-prevention contexts. It employs two online experiments to compare AI-generated versus human-generated messages, examining evaluation via effects perceptions and preference via ranking (Study 1) and a moderation-enabled selection task (Study 2). The findings reveal a small bias against AI-generated content when the source is disclosed, with negative attitudes toward AI amplifying evaluation differences, while ranking/selection effects are weaker or context-dependent. These results contribute to source-effects theory in health communication and offer practical guidance for labeling AI-generated content in public health campaigns.
Abstract
Advancements in artificial intelligence (AI) over the last decade demonstrate that machines can exhibit communicative behavior and influence how humans think, feel, and behave. In fact, the recent development of ChatGPT has shown that large language models (LLMs) can be leveraged to generate high-quality communication content at scale and across domains, suggesting that they will be increasingly used in practice. However, many questions remain about how knowing the source of the messages influences recipients' evaluation of and preference for AI-generated messages compared to human-generated messages. This paper investigated this topic in the context of vaping prevention messaging. In Study 1, which was pre-registered, we examined the influence of source disclosure on people's evaluation of AI-generated health prevention messages compared to human-generated messages. We found that source disclosure (i.e., labeling the source of a message as AI vs. human) significantly impacted the evaluation of the messages but did not significantly alter message rankings. In a follow-up study (Study 2), we examined how the influence of source disclosure may vary by the participants' negative attitudes towards AI. We found a significant moderating effect of negative attitudes towards AI on message evaluation, but not for message selection. However, for those with moderate levels of negative attitudes towards AI, source disclosure decreased the preference for AI-generated messages. Overall, the results of this series of studies showed a slight bias against AI-generated messages once the source was disclosed, adding to the emerging area of study that lies at the intersection of AI and communication.