Abstract for: Bridging the Unavoidable A Priori: A Framework for Comparative Causal Modeling

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 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. 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. The general framework defines functions that map dynamic and static variables into a measurement model and set of observations. The framework is illustrated with the "Limits to Growth" SD model and "“Industrialization and Political Democracy” SEM model. Defining the mathematical space for bridging these two methods provides a means to generate systems in a systematic way and conduct simulation studies to explore the implications of various assumptions on causal inferences from modeling and data. This becomes even more critical today when we consider the implications of implementing AI/ML models in systems that impact society because more than ever before, we need to understand how the assumptions we make and hence our biases might translate into algorithmic biases.