Labeling AI-Generated Content: Promises, Perils, and Future Directions

Abstract

Labeling is a commonly proposed strategy for reducing the risks of generative artificial intelligence (AI). This approach involves applying visible content warnings to alert users to the presence of AI-generated media online (e.g., on social media, news sites, or search engines). Although there is little direct evidence regarding the effectiveness of labeling AI-generated media, a large volume of academic work suggests that warning labels can substantially reduce people's belief in and sharing of content debunked by professional fact-checkers. Thus, there is reason to believe that labeling could help inform members of the public about AI-generated media. In this paper, we provide a framework for helping policymakers, platforms, and practitioners weigh various factors related to the labeling of AI-generated content online. First, we argue that, before developing labeling programs and policies related to generative AI, stakeholders must establish the objective(s) that labeling is intended to accomplish. Here, we distinguish two such goals: (1) communicating to viewers the process by which a given piece of content was created or edited (i.e., with or without using generative AI tools) versus (2) diminishing the likelihood that content misleads or deceives its viewers (a result that does not necessarily depend on whether the content was created using AI). Next, we summarize results from two large-scale experiments demonstrating that labeling can, under certain conditions, meaningfully decrease individuals' likelihood of believing and engaging with misleading, AI-generated images. Finally, we highlight several important issues and challenges that must be considered when designing, evaluating, and implementing labeling policies and programs, including the need to (1) determine what types of content to label and how to reliably identify this content at scale, (2) consider the inferences viewers will draw about both labeled and unlabeled content, and (3) evaluate the efficacy of labeling approaches across contexts.