机器学习模型的衡量指标_在机器学习模型中衡量公平性

In our previous article, we gave an in-depth review on how to explain biases in data. The next step in our fairness journey is to dig into how to detect biased machine learning models.

在上一篇文章中，我们对如何解释数据偏差进行了深入的回顾。 我们公平之旅的下一步是深入研究如何检测偏向机器学习模型。

However, before detecting (un)fairness in machine learning, we first need to be able to define it. But fairness is an equivocal notion — it can be expressed in various ways to reflect the specific circumstances of the use case or the ethical perspectives of the stakeholders. Consequently, there can’t be a consensus in research about what fairness in machine learning actually is.

但是，在检测机器学习中的(不)公平性