AI/ML models have rapidly gained prominence as innovations for solving previously unsolved problems and their unintended consequences from amplifying human biases. Advocates for responsible AI/ML have sought ways to draw on the richer causal models of system dynamics to better inform the development of responsible AI/ML. However, a major barrier to advancing this work is the difficulty of bringing together methods rooted in different underlying assumptions (i.e., Dana Meadow's "the unavoidable a priori"). This paper brings system dynamics and structural equation modeling together into a common mathematical framework that can be used to generate systems from distributions, develop methods, and compare results to inform the underlying epistemology of system dynamics for data science and AI/ML applications.

翻译：人工智能/机器学习模型作为解决先前未解问题及其因放大人类偏见而产生意外后果的创新手段，已迅速获得显著地位。负责任人工智能/机器学习的倡导者试图借鉴系统动力学中更丰富的因果模型，以更好地指导负责任人工智能/机器学习的发展。然而，推进这项工作的主要障碍在于难以整合基于不同基本假设（即Dana Meadow所提出的“不可避免的先验”）的方法。本文通过将系统动力学与结构方程建模纳入统一的数学框架，该框架可用于从分布中生成系统、开发方法，并通过比较结果为数据科学及人工智能/机器学习应用中的系统动力学认识论基础提供参考。