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.

本文综述了提高可解释机器学习模型的技术，并讨论了未来研究的关键问题，如设计用户友好的解释和开发全面的评估指标，以进一步推动可解释机器学习的发展。

可解释机器学习技术

Techniques for Interpretable Machine Learning

Training complex machine learning models for prediction often requires a
large amount of data that is not always readily available. Leveraging these
external datasets from related but different sources is therefore an important
task if good predictive models are to be built for deployment in settings where
data can be rare. In this paper we propose a novel approach to the problem in
which we use multiple GAN architectures to learn to translate from one dataset
to another, thereby allowing us to effectively enlarge the target dataset, and
therefore learn better predictive models than if we simply used the target
dataset. We show the utility of such an approach, demonstrating that our method
improves the prediction performance on the target domain over using just the
target dataset and also show that our framework outperforms several other
benchmarks on a collection of real-world medical datasets.

本论文提出了一种使用多个 GAN 架构进行数据集翻译的新方法，从而有效扩大目标数据集，从而在预测性能上优于仅使用目标数据集，并且在一系列真实医学数据集上优于几个其他基准。