# Copying Machine Learning Classifiers

arXiv: https://arxiv.org/abs/1903.01879 · HTML: https://mlsanity.com/paper/1903.01879/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [Desiderata for Explainable AI in statistical production systems of the European Central Bank](https://mlsanity.com/paper/2107.08045/) (2021)
- [A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers](https://mlsanity.com/paper/2302.02667/) (2023)
- [Introducing explainable supervised machine learning into interactive feedback loops for statistical production system](https://mlsanity.com/paper/2202.03212/) (2022)
- [Does Explainable AI Have Moral Value?](https://mlsanity.com/paper/2311.14687/) (2023)
- [Smoothing the Black-Box: Signed-Distance Supervision for Black-Box Model Copying](https://mlsanity.com/paper/2601.20773/) (2026)

## Abstract (arXiv)

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.