Techniques for Interpretable Machine Learning
This is an incremental survey paper that synthesizes existing work for researchers and practitioners in interpretable machine learning.
The paper provides a survey of existing techniques to increase the interpretability of machine learning models, addressing the problem that humans cannot understand complex model behaviors and decisions, and discusses future challenges like user-friendly explanations and evaluation metrics.
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.