Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.

中文翻译：

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 人-AI 协同进化


人类与 AI 的协同进化被定义为人类和 AI 算法不断相互影响的过程，越来越成为我们社会的特征，但在人工智能和复杂性科学文献中研究不足。推荐系统和助手在人类与 AI 的协同进化中发挥着重要作用，因为它们渗透到日常生活的许多方面，并通过在线平台影响人类的选择。用户和 AI 之间的交互会导致潜在的无休止的反馈循环，其中用户的选择会生成数据来训练 AI 模型，这反过来又会塑造后续的用户偏好。与传统的人机交互相比，这种人机交互具有独特的特征，并会产生复杂且通常是“意外”的系统结果。本文介绍了人类与 AI 的协同进化，作为 AI 与复杂性科学之间交叉点的新研究领域的基石，该领域的重点是人类与 AI 反馈回路的理论、实证和数学研究。在此过程中，我们：（i） 概述现有方法的优缺点，并强调捕获反馈循环机制的缺点和潜在方法;（ii） 提出对复杂性科学、人工智能和社会之间交叉点的反思;（iii） 为不同的人类人工智能生态系统提供真实示例;（iv） 说明创建这样一个研究领域的挑战，在越来越多的抽象层面（即科学、法律和社会政治）上将其概念化。