  Camera images are ubiquitous in machine learning research. They also play a
central role in the delivery of important services spanning medicine and
environmental surveying. However, the application of machine learning models in
these domains has been limited because of robustness concerns. A primary
failure mode are performance drops due to differences between the training and
deployment data. While there are methods to prospectively validate the
robustness of machine learning models to such dataset drifts, existing
approaches do not account for explicit models of the primary object of
interest: the data. This limits our ability to study and understand the
relationship between data generation and downstream machine learning model
performance in a physically accurate manner. In this study, we demonstrate how
to overcome this limitation by pairing traditional machine learning with
physical optics to obtain explicit and differentiable data models. We
demonstrate how such data models can be constructed for image data and used to
control downstream machine learning model performance related to dataset drift.
The findings are distilled into three applications. First, drift synthesis
enables the controlled generation of physically faithful drift test cases to
power model selection and targeted generalization. Second, the gradient
connection between machine learning task model and data model allows advanced,
precise tolerancing of task model sensitivity to changes in the data
generation. These drift forensics can be used to precisely specify the
acceptable data environments in which a task model may be run. Third, drift
optimization opens up the possibility to create drifts that can help the task
model learn better faster, effectively optimizing the data generating process
itself. A guide to access the open code and datasets is available at
https://github.com/aiaudit-org/raw2logit.
