Increasing the effectiveness of charitable giving with AI-generated persuasion

Charitable donations frequently fail to maximize cost-effectiveness (the amount of good a donation does per dollar). This failure is often attributed to charitable motivations being affective and thus insensitive to evidence-based arguments. We challenge this perspective, hypothesizing that evidence can substantially increase effective giving—if that evidence is sufficiently compelling. We test this prediction in a pre-registered experiment (N = 1,949 Americans) by leveraging the ability of artificial intelligence large language models (LLMs) to generate persuasive content. Participants allocated $1 between their favorite charity and a highly effective charity (the Against Malaria Foundation), before and after an LLM dialogue or a static LLM message advocating for the effective charity, or a control conversation. The LLM dialogue and static LLM message both significantly increased effective donations (45.9% and 28.7%, respectively) in comparison to control, while the LLM dialogue also shifted moral attitudes. Effective giving can be meaningfully increased through evidence-based persuasion with LLMs.