On the one hand, complexity science and enactive and embodied cognitive science approaches emphasize that people, as complex adaptive systems, are ambiguous, indeterminable, and inherently unpredictable. On the other, Machine Learning (ML) systems that claim to predict human behaviour are becoming ubiquitous in all spheres of social life. I contend that ubiquitous Artificial Intelligence (AI) and ML systems are close descendants of the Cartesian and Newtonian worldview in so far as they are tools that fundamentally sort, categorize, and classify the world, and forecast the future. Through the practice of clustering, sorting, and predicting human behaviour and action, these systems impose order, equilibrium, and stability to the active, fluid, messy, and unpredictable nature of human behaviour and the social world at large. Grounded in complexity science and enactive and embodied cognitive science approaches, this article emphasizes why people, embedded in social systems, are indeterminable and unpredictable. When ML systems “pick up” patterns and clusters, this often amounts to identifying historically and socially held norms, conventions, and stereotypes. Machine prediction of social behaviour, I argue, is not only erroneous but also presents real harm to those at the margins of society.

一方面，复杂性科学和生成和具身的认知科学方法强调人作为复杂的适应系统，是模糊的、不可确定的和本质上不可预测的。另一方面，声称可以预测人类行为的机器学习 (ML) 系统正在社会生活的各个领域变得无处不在。我认为无处不在的人工智能 (AI) 和 ML 系统是笛卡尔和牛顿世界观的密切后代，因为它们是从根本上对世界进行分类、分类和分类以及预测未来的工具。通过对人类行为和行动进行聚类、分类和预测的实践，这些系统将秩序、平衡和稳定性强加给人类行为和整个社会世界的活跃、流动、混乱和不可预测的本质。本文以复杂性科学和主动和体现的认知科学方法为基础，强调为什么嵌入社会系统的人是无法确定和不可预测的。当 ML 系统“拾取”模式和集群时，这通常等同于识别历史和社会持有的规范、惯例和刻板印象。我认为，机器对社会行为的预测不仅是错误的，而且对那些处于社会边缘的人造成了真正的伤害。