Uncovering the Fairness of AI: Exploring Focal Point, Inequality Aversion, and Altruism in ChatGPT's Dictator Game Decisions

Eléonore Dodivers and Ismaël Rafaï
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Eléonore Dodivers: Université Côte d'Azur, CNRS, GREDEG, France
Ismaël Rafaï: Toulouse School of Economics, Toulouse School of Management

No 2025-09, GREDEG Working Papers from Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France

Abstract: This paper investigates Artificial intelligence Large Language Models (AI-LLM) social preferences’ in Dictator Games. Brookins and Debacker (2024, Economics Bulletin) previously observed a tendency of ChatGPT-3.5 to give away half its endowment in a standard Dictator Game and interpreted this as an expression of fairness. We replicate their experiment and introduce a multiplicative factor on donations which varies the efficiency of the transfer. Varying transfer efficiency disentangles three donation explanations (inequality aversion, altruism, or focal point). Our results show that ChatGPT-3.5 donations should be interpreted as a focal point rather than the expression of fairness. In contrast, a more advanced version (ChatGPT-4o) made decisions that are better explained by altruistic motives than inequality aversion. Our study highlights the necessity to explore the parameter space, when designing experiments to study AI-LLM preferences.

Keywords: Artificial Intelligence; Large Language Models; Dictator Games; Experimental Economics; Social Preferences (search for similar items in EconPapers)
JEL-codes: C02 C91 D90 O33 (search for similar items in EconPapers)
Pages: 9 pages
Date: 2025-04
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