Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision. Although many approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. We provide a survey covering existing techniques to increase the interpretability of machine learning models. We also discuss crucial issues that the community should consider in future work such as designing user-friendly explanations and developing comprehensive evaluation metrics to further push forward the area of interpretable machine learning.
@article{arxiv.1808.00033,
title = {Techniques for Interpretable Machine Learning},
author = {Mengnan Du and Ninghao Liu and Xia Hu},
journal= {arXiv preprint arXiv:1808.00033},
year = {2019}
}
Comments
Accepted by Communications of the ACM (CACM), Review Article