机器学习Explainability vs Interpretability

The difference between machine learning explainability and interpretability

In the context of machine learning and artificial intelligence, explainability and interpretability are often used interchangeably. While they are very closely related, it’s worth unpicking the differences, if only to see how complicated things can get once you start digging deeper into machine learning systems.

Interpretability is about the extent to which a cause and effect can be observed within a system. Or, to put it another way, it is the extent to which you are able to predict what is going to happen, given a change in input or algorithmic parameters. It’s being able to look at an algorithm and go yep, I can see what’s happening here.

Explainability, meanwhile, is the extent to which the internal mechanics of a machine or deep learning system can be explained in human terms. It’s easy to miss the subtle difference with interpretability, but consider it like this: interpretability is about being able to discern the mechanics without necessarily knowing why. Explainability is being able to quite literally explain what is happening.

Think of it this way: say you’re doing a science experiment at school. The experiment might be interpretable insofar as you can see what you’re doing, but it is only really explainable once you dig into the chemistry behind what you can see happening.

That might be a little crude, but it is nevertheless a good starting point for thinking about how the two concepts relate to one another.

机器学习的可解释性和可解读性之间的区别

在机器学习和人工智能的背景下，explainability 和 interpretability 经常互换使用。尽管它们之间有着密切的联系，但是值得一提的是，它们之间的差异，仅仅是为了看看一旦您开始更深入地研究机器学习系统，事情就会变得多么复杂。

interpretability大约是在系统中可以观察到因果关系的程度。或者换句话说，在输入或算法参数发生变化的情况下，它是您能够预测将要发生的情况的程度。

同时，explainability是可以用人类术语解释机器或深度学习系统的内部机制的程度。很容易错过explainability 和 interpretability的细微差别，但您应该这样考虑：interpretability是指能够辨认机制而不必知道原因。Explainability能够从字面上解释发生的事情。

这样想：假设您正在学校进行科学实验。就您所看到的所做的事情而言，该实验可能是interpretable，但是只有当您深入了解所发生的事情背后的化学反应时，该实验才能真正地得到explainable。

这可能有点粗糙，但这仍然是思考这两个概念如何相互关联的一个很好的起点。