Machine learning models are increasingly used in the industry to make decisions such as credit insurance approval. Some people may be tempted to manipulate specific variables, such as the age or the salary, in order to get better chances of approval. In this ongoing work, we propose to discuss, with a first proposition, the issue of detecting a potential local adversarial example on classical tabular data by providing to a human expert the locally critical features for the classifier's decision, in order to control the provided information and avoid a fraud.
@article{arxiv.1809.02397,
title = {Detecting Potential Local Adversarial Examples for Human-Interpretable Defense},
author = {Xavier Renard and Thibault Laugel and Marie-Jeanne Lesot and Christophe Marsala and Marcin Detyniecki},
journal= {arXiv preprint arXiv:1809.02397},
year = {2018}
}
Comments
presented at 2018 ECML/PKDD Workshop on Recent Advances in Adversarial Machine Learning (Nemesis 2018), Dublin, Ireland