  Security and ethics are both core to ensuring that a machine learning system
can be trusted. In production machine learning, there is generally a hand-off
from those who build a model to those who deploy a model. In this hand-off, the
engineers responsible for model deployment are often not privy to the details
of the model and thus, the potential vulnerabilities associated with its usage,
exposure, or compromise. Techniques such as model theft, model inversion, or
model misuse may not be considered in model deployment, and so it is incumbent
upon data scientists and machine learning engineers to understand these
potential risks so they can communicate them to the engineers deploying and
hosting their models. This is an open problem in the machine learning community
and in order to help alleviate this issue, automated systems for validating
privacy and security of models need to be developed, which will help to lower
the burden of implementing these hand-offs and increasing the ubiquity of their
adoption.
