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

翻译：随着人工智能系统日益融入我们的生活，它们的存在引发了影响人类行为、决策和社会互动的交互。现有理论研究主要聚焦于人与人之间的互动，忽视了AI存在所引发的独特动态。本文借助演化博弈论方法，研究不同形式的AI如何影响人类群体中合作行为的演化——在人类参与一次性囚徒困境博弈的充分混合群体和结构化群体中均进行了分析。我们发现：无条件帮助所有人（包括背叛者）的撒玛利亚式AI代理，相较于只帮助被认为值得合作者的歧视性AI，更能促进人类群体中的高水平合作，尤其在变化缓慢（选择强度较弱）的社会中。直观而言，在快速变化（选择强度较强）的社会中，歧视性AI比撒玛利亚式AI更能促进高水平合作。