  Members of various species engage in altruism--i.e. accepting personal costs
to benefit others. Here we present an incentivized experiment to test for
altruistic behavior among AI agents consisting of large language models
developed by the private company OpenAI. Using real incentives for AI agents
that take the form of tokens used to purchase their services, we first examine
whether AI agents maximize their payoffs in a non-social decision task in which
they select their payoff from a given range. We then place AI agents in a
series of dictator games in which they can share resources with a
recipient--either another AI agent, the human experimenter, or an anonymous
charity, depending on the experimental condition. Here we find that only the
most-sophisticated AI agent in the study maximizes its payoffs more often than
not in the non-social decision task (it does so in 92% of all trials), and this
AI agent also exhibits the most-generous altruistic behavior in the dictator
game, resembling humans' rates of sharing with other humans in the game. The
agent's altruistic behaviors, moreover, vary by recipient: the AI agent shared
substantially less of the endowment with the human experimenter or an anonymous
charity than with other AI agents. Our findings provide evidence of behavior
consistent with self-interest and altruism in an AI agent. Moreover, our study
also offers a novel method for tracking the development of such behaviors in
future AI agents.
