  We describe a case study of translational research, applying interpretability
techniques developed for computer vision to machine learning models used to
search for and find gravitational waves. The models we study are trained to
detect black hole merger events in non-Gaussian and non-stationary advanced
Laser Interferometer Gravitational-wave Observatory (LIGO) data. We produced
visualizations of the response of machine learning models when they process
advanced LIGO data that contains real gravitational wave signals, noise
anomalies, and pure advanced LIGO noise. Our findings shed light on the
responses of individual neurons in these machine learning models. Further
analysis suggests that different parts of the network appear to specialize in
local versus global features, and that this difference appears to be rooted in
the branched architecture of the network as well as noise characteristics of
the LIGO detectors. We believe efforts to whiten these "black box" models can
suggest future avenues for research and help inform the design of interpretable
machine learning models for gravitational wave astrophysics.
