  The field of high-energy physics (HEP), along with many scientific
disciplines, is currently experiencing a dramatic influx of new methodologies
powered by modern machine learning techniques. Over the last few years, a
growing body of HEP literature has focused on identifying promising
applications of deep learning in particular, and more recently these techniques
are starting to be realized in an increasing number of experimental
measurements. The overall conclusion from this impressive and extensive set of
studies is that rarer and more complex physics signatures can be identified
with the new set of powerful tools from deep learning. However, there is an
unstudied systematic risk associated with combining the traditional HEP
workflow and deep learning with high-dimensional data. In particular,
calibrating and validating the response of deep neural networks is in general
not experimentally feasible, and therefore current methods may be biased in
ways that are not covered by current uncertainty estimates. By borrowing ideas
from AI safety, we illustrate these potential issues and propose a method to
bound the size of unaccounted for uncertainty. In addition to providing a
pragmatic diagnostic, this work will hopefully begin a dialogue within the
community about the robust application of deep learning to experimental
analyses.
