  We study model-agnostic copies of machine learning classifiers. We develop
the theory behind the problem of copying, highlighting its differences with
that of learning, and propose a framework to copy the functionality of any
classifier using no prior knowledge of its parameters or training data
distribution. We identify the different sources of loss and provide guidelines
on how best to generate synthetic sets for the copying process. We further
introduce a set of metrics to evaluate copies in practice. We validate our
framework through extensive experiments using data from a series of well-known
problems. We demonstrate the value of copies in use cases where desiderata such
as interpretability, fairness or productivization constrains need to be
addressed. Results show that copies can be exploited to enhance existing
solutions and improve them adding new features and characteristics.
