Trust, Delegation, and Alignment in Human–AI Decision Making

This paper studies delegation to artificial intelligence in a setting where human principals retain the consequences of delegated choices. Participants wrote prompts instructing ChatGPT-4o mini how to choose on their behalf in three canonical economic domains: risky choice, intertemporal choice, and social allocation. We then elicited the compensation participants required to let the AI’s choices count for payment and compared participants’ own choices to choices generated from their prompts. The design produces two central empirical objects: a revealed measure of reluctance to delegate, captured by willingness to accept compensation for AI delegation, and a behavioral measure of alignment, captured by the share of decisions on which the participant and AI made the same choice. We supplement the original experiment with two benchmarks: a human-agent follow-up in which other participants attempted to implement the same prompts, and an ex post robustness exercise using GPT-5.5. The results show substantial reluctance to delegate despite moderate-to-high alignment, and they suggest that misalignment reflects not only model limitations but also the difficulty of communicating delegable preferences through short natural-language prompts.