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

TL;DR

This paper uses evolutionary game theory to analyze how different types of AI influence cooperation in human populations, revealing that Samaritan AI fosters more cooperation in slow societies, while Discriminatory AI does so in fast societies.

Contribution

It introduces a novel evolutionary game theory model to compare the impact of Samaritan and Discriminatory AI on human cooperation dynamics.

Findings

Samaritan AI promotes higher cooperation in slow societies.

Discriminatory AI enhances cooperation in fast societies.

AI type effects depend on societal change speed.

Abstract

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.