Structural Causal Models as Boundary Objects in AI System Development
Artificial Intelligence (AI), and especially machine learning can be used to find statistical patterns in datasets with thousands of variables with ease. But an understanding of causality is difficult to learn for a machine. For humans however, realising causal relations is often not a difficult process, as we can refer to experience or scientific knowledge. Here we propose the use of structural causal models, represented through direct acyclic graphs, to design, determine, and communicate causal relations hidden beyond the statistical models of an AI. The idea is to make human insight in causal relations explicit and use this knowledge during AI system development. In a joint-industry project we discovered that structural causal models can serve as living boundary objects that facilitate coordination of domain experts, data scientists, systems engineers, and AI experts in AI system development.
Added 2026-04-21