We propose a novel approach to understanding the decision making of complex machine learning models (e.g., deep neural networks) using a combination of probably approximately correct learning (PAC) and a logic inference methodology called syntax-guided synthesis (SyGuS). We prove that our framework produces explanations that with a high probability make only few errors and show empirically that it is effective in generating small, human-interpretable explanations.
@article{arxiv.2009.08770,
title = {Probably Approximately Correct Explanations of Machine Learning Models via Syntax-Guided Synthesis},
author = {Daniel Neider and Bishwamittra Ghosh},
journal= {arXiv preprint arXiv:2009.08770},
year = {2020}
}