Observational fairness

Equality is often seen as a purely qualitative issue, and as such, it's often dismissed by quantitative-minded modelers. As this section will show, equality can be seen from a quantitative perspective, too. Consider a classifier, c, with input X, some sensitive input, A, a target, Y and output C. Usually, we would denote the classifier output as Observational fairness, but for readability, we follow CS 294 and name it C.

Let's say that our classifier is being used to decide who gets a loan. When would we consider this classifier to be fair and free of bias? To answer this question, picture two demographics, group A and B, both loan applicants. Given ...

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