Research Area:  Machine Learning

Abstract:

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.

Keywords:  

Author(s) Name:  Mengnan Du , Ninghao Liu , Xia Hu

Publisher name:  ACM

DOI:  10.1145/3359786

Volume Information:  Volume 63,Issue 1,January 2020, pp 68–77,

Paper Link:   https://dl.acm.org/doi/fullHtml/10.1145/3359786