Addressing climate change skepticism and inaction using human-AI dialogues

Despite scientific consensus on human-caused climate change, skepticism and inaction persist. We ask whether facts and evidence - tailored to address each person’s specific concerns by an AI model - can address climate skepticism and motivate climate action. Participants first described their main climate change reservation. Of the N = 1,947 who articulated reservations, the most prevalent were the belief that climate change has natural causes (15%), feeling overwhelmed by the problem (10%), and concerns about the economic consequences of climate policies (8%). Participants were then randomized to (1) have a conversation with a Large Language Model (LLM) that was given the goal of addressing their climate reservations, (2) discuss an irrelevant topic with the LLM (i.e., control), or (3) receive static information about the scientific consensus around climate change (i.e., “standard-of-care”) after the control conversation. The LLM treatment significantly reduced participants’ conviction in their specific reservations, while consensus messaging had no significant effect. Both treatments led to significant increases in general pro-climate attitudes, but the LLM treatment was significantly more effective than consensus messaging - particularly for increasing willingness to make sacrifices to address climate change. Pre-treatment beliefs did not moderate treatment effectiveness, and the treatment substantially reduced Republicans’ reservations (although less than for Independents or Democrats). We also find evidence that roughly 35% to 40% of the LLM treatment effect persisted after one month. These findings highlight that it is possible to reach many of the climate skeptical or hesitant with the right facts and evidence.