Considerations for Evaluation and Generalization in Interpretable Machine Learning
Considerations for Evaluation and Generalization in Interpretable Machine Learning
复制标题
可解释机器学习中评估和泛化的注意事项
DOI：
10.1007/978-3-319-98131-4_1
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发表时间：
2018
期刊：
影响因子：
9.9
通讯作者：
Been Kim
中科院分区：
文献类型：
--
作者：
F. Doshi;Been Kim
随着机器学习系统变得无处不在，人们对可解释机器学习的兴趣激增：为输出提供解释的系统。这些解释通常用于定性评估其他标准，如安全或不歧视。然而，尽管人们对可解释性很感兴趣，但对于什么是可解释的机器学习以及如何对其进行测量和评估，人们几乎没有达成共识。在本文中，我们讨论了可解释性的定义，并描述了何时需要可解释性（以及何时不需要）。最后，我们讨论了严格评估的分类法，以及对研究人员的建议。最后，我们将讨论新研究人员的开放性问题和具体问题。
As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpretability, there is little consensus on what interpretable machine learning is and how it should be measured and evaluated. In this paper, we discuss a definitions of interpretability and describe when interpretability is needed (and when it is not). Finally, we talk about a taxonomy for rigorous evaluation, and recommendations for researchers. We will end with discussing open questions and concrete problems for new researchers.