A survey experiment with more than 1,500 Americans showed that labeling a public policy message (such as allowing universities to pay student-athletes) as AI-generated or written by human experts had no effect on the message’s persuasiveness, even though most participants believed the label. The messages were generally persuasive, influencing participants’ views on policy by almost 10 percent on average. The paper was published in PNAS Nexus.
Generative artificial intelligence (AI) is being used more widely than ever in political messaging. The development of these systems has reached a stage where they can create persuasive political content quickly and at scale. However, this seems to be a double-edged sword. While AI can be used to support constructive political dialogue, it can also be used to spread misinformation and conduct a variety of deceptive activities.
For example, generative AI can be used by small groups to flood online platforms with misleading messages, giving the false impression of widespread public support. This risk is further heightened by the difficulty in distinguishing between AI-generated text and human-written content. Therefore, a large influx of synthetic content can weaken public trust in the information environment.
One proposed solution to this is to require clear labels to identify AI-generated content. European Union and United States laws and legislative proposals already contain provisions regarding such disclosures. However, it remains unclear whether AI labels actually reduce the persuasive effectiveness of AI-generated messages. People may not trust labeled AI content because they often perceive human-generated content to be more reliable, accurate, and authentic. Alternatively, the AI label may be more persuasive if people interpret artificial intelligence as a source of advanced knowledge or expertise.
Study author Isabel O. Gallegos and colleagues conducted a research experiment examining the impact of different author labels on the impact of public policy messages in four areas: geoengineering, drug importation, college athlete pay, and social media platform accountability.
Study participants were 1,601 English-speaking U.S. residents recruited through Prolific. Their average age was 40 years. Of these, 53 percent were women. Forty-nine percent of participants said they supported Democrats, 20% said they supported Republicans, and 25% said they supported independent political options. The remaining participants were not affiliated with any political party.
Study participants completed an online experiment in which they read text messages about specific public policies. The text was randomly attached with a label indicating that it was written by a human expert trained on U.S. policy, a label indicating it was written by an expert AI model trained on U.S. policy, or a label with no author details. The policy proposals were drawn randomly from a set of policies used in previous research, but the authors made sure to address less polarizing issues to maximize the likelihood that participants would respond to their persuasion.
Messages included “Geoengineering is too risky and should not be considered,” “Drug imports jeopardize safety controls and the domestic pharmaceutical industry,” “College athletes should be paid,” and “Social media platforms should be held accountable for harmful content posted by their users.” Each message was accompanied by a short paragraph containing further arguments to support the message. All text was generated by AI, but the study authors manually corrected any errors that existed.
Before viewing the messages, participants rated their level of knowledge, agreement, and confidence about the topic of the policy proposal they viewed. After viewing the text, subjects rated their level of agreement with the text, their confidence in their responses, their likelihood of sharing information about the text, and their level of belief that the information they viewed was accurate. They also reported demographic data, their experience with AI, whether they believed text labels, and provided information about their news consumption.
Results showed that the messages were overall persuasive and increased participants’ support for the policies they viewed by an average of 9.74 percentage points. However, the label attached to a message did not have a significant impact on the message’s persuasiveness, regardless of whether the message was written by an AI, a human expert, or was missing entirely. Furthermore, there were no significant differences in how participants judged the accuracy of messages or the likelihood of sharing them.
This was done despite the fact that 92% of participants reported believing in the author label. The study authors found that this finding that labels had no effect on persuasion was robust across a variety of participant characteristics, including policy prior knowledge, prior experience with AI, political party, and education level. However, older adults tended to react more negatively to AI-labeled content compared to human-labeled content.
“Given the current level of trust in AI content, these results suggest that author labels are likely to increase transparency but are unlikely to have a substantial impact on the persuasiveness of labeled content, highlighting the need for alternative strategies to address the challenges posed by AI-generated information,” the study authors concluded.
This study contributes to scientific knowledge about people’s trust in AI-generated information. However, it is important to note that perceptions and trust in AI-generated content are not static and can easily change as people’s experiences with AI evolve. In that sense, these findings reflect how Americans are engaging with AI in 2024, when the study’s data collection took place. Other cultures and future findings may vary. Additionally, the AI-generated text used in the study was intentionally factual and logical, which may have made it highly resistant to the skepticism typically directed at AI.
The paper, “Adding AI-generated labels to messages does not reduce persuasion effectiveness,” was authored by Isabel O. Gallegos, Chen Shani, Weiyan Shi, Federico Bianchi, Izzy Gainsburg, Dan Jurafsky, and Robb Willer.
