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As artificial intelligence (AI) systems are increasingly embedded in ourlives, their presence leads to interactions that shape our behaviour,decision-making, and social interactions. Existing theoretical research hasprimarily focused on human-to-human interactions, overlooking the uniquedynamics triggered by the presence of AI. In this paper, resorting to methodsfrom evolutionary game theory, we study how different forms of AI influence theevolution of cooperation in a human population playing the one-shot Prisoner'sDilemma game in both well-mixed and structured populations. We found thatSamaritan AI agents that help everyone unconditionally, including defectors,can promote higher levels of cooperation in humans than Discriminatory AI thatonly help those considered worthy/cooperative, especially in slow-movingsocieties where change is viewed with caution or resistance (small intensitiesof selection). Intuitively, in fast-moving societies (high intensities ofselection), Discriminatory AIs promote higher levels of cooperation thanSamaritan AIs.

Discriminatory or Samaritan -- which AI is needed for humanity? An Evolutionary Game Theory Analysis of Hybrid Human-AI populations