How convincing are AI-generated moral arguments for climate action?

Abstract

Mobilizing broad support for climate action is paramount for solving the climate crisis. Research suggests that people can be persuaded to support climate action when presented with certain moral arguments, but which moral arguments are most convincing across the population? With this pilot study, we aim to understand which types of moral arguments based on an extended Moral Foundation Theory are most effective at convincing people to support climate action. Additionally, we explore to what extent Generative Pre-trained Transformer 3 (GPT-3) models can be employed to generate bespoke moral statements. We find statements appealing to compassion, fairness and good ancestors are the most convincing to participants across the population, including to participants, who identify as politically right-leaning and who otherwise respond least to moral arguments. Negative statements appear to be more convincing than positive ones. Statements appealing to other moral foundations can be convincing, but only to specific social groups. GPT-3-generated statements are generally more convincing than human-generated statements, but the large language model struggles with creating novel arguments.