  We consider how human-centered causal theories and tools from the dynamical
systems literature can be deployed to guide the representation of data when
training neural networks for complex classification tasks. Specifically, we use
simulated data to show that training a neural network with a data
representation that makes explicit the invariant structural causal features of
the data generating process of an epidemic system improves out-of-distribution
(OOD) generalization performance on a classification task as compared to a more
naive approach to data representation. We take these results to demonstrate
that using human-generated causal knowledge to reduce the epistemic uncertainty
of ML developers can lead to more well-specified ML pipelines. This, in turn,
points to the utility of a dynamical systems approach to the broader effort
aimed at improving the robustness and safety of machine learning systems via
improved ML system development practices.

Machine Learning;Human-Computer Interaction;Methodology