论文标题
ML解释性：简单并不容易
ML Interpretability: Simple Isn't Easy
论文作者
论文摘要
ML模型的解释性很重要，但尚不清楚它等于什么。到目前为止，大多数哲学家已经讨论了黑盒模型（例如神经网络）的不可解释性，以及旨在使这些模型更透明的可解释AI等方法。本文的目的是通过专注于“可解释性谱”的另一端来阐明可解释性的性质。某些模型，线性模型和决策树的高度可解释的原因将被检查，以及更多的通用模型，火星和GAM如何保留一定程度的解释性。我发现，尽管我们如何获得可解释性存在异质性，但在特定情况下可以清楚地阐明哪些解释性。
The interpretability of ML models is important, but it is not clear what it amounts to. So far, most philosophers have discussed the lack of interpretability of black-box models such as neural networks, and methods such as explainable AI that aim to make these models more transparent. The goal of this paper is to clarify the nature of interpretability by focussing on the other end of the 'interpretability spectrum'. The reasons why some models, linear models and decision trees, are highly interpretable will be examined, and also how more general models, MARS and GAM, retain some degree of interpretability. I find that while there is heterogeneity in how we gain interpretability, what interpretability is in particular cases can be explicated in a clear manner.
