Do people apply different norms to humans and large language models acting on their behalf? Evidence from norm elicitations in two canonical economic games

Abstract

While delegating tasks to large language models (LLMs) can save people time, there is growing evidence that offloading tasks to such models produces social costs. We elicit appropriateness of behavior in two canonical economic games to study whether people have different norms for humans and LLMs acting on their behalf. More specifically, we study the social appropriateness of a spectrum of possible behaviors: when LLMs divide resources on our behalf (Dictator Game and Ultimatum Game) and when they evaluate the fairness of resource splits (Ultimatum Game). We use the Krupka-Weber norm elicitation task to detect possible shifts in social appropriateness ratings. Results of two pre-registered and incentivized experimental studies using representative samples from the UK and US (N = 2,658) show three key findings. First, in the Dictator Game, the most socially appropriate allocation remains the equal split when the Dictator is a human or an LLM acting on behalf of a human, but self-serving allocations are judged less appropriate when implemented by an LLM. Second, in the Ultimatum Game, rejecting offers from an LLM Proposer is judged as more appropriate than rejecting identical offers from a human Proposer, with this difference concentrated among relatively generous offers. Lastly, rejection decisions implemented by an LLM Responder are judged no less appropriate than identical rejection decisions implemented by a human Responder. Post hoc, these patterns are consistent with the notion that people grant LLMs bounded agency in the investigated resource-allocation scenarios. Our findings have implications for the design of AI systems.

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