Risk Scores, Fairness, and Impossibility
/via https://fairmlclass.github.io/1.html#/4 Over the next few years, Deep Learning is going to get embedded in our daily lives in matters great and small, from self-driving cars (it’ll happen?!) to medical diagnosis, to, well, all sorts of stuff. As we go down this road though, we really need to think through what exactly the algorithms are telling us. And the reason for this is that the information that we get will, by definition, be biased in one form or the other. Ok, “biased” a bit of a loaded term, but I use it advisedly. I could mean that these algorithms get used for fraud , but I don’t. What I do mean — which is actually worse — is that with risk-scoring, it is mathematically impossible to actually be “fair” across multiple groups! (Before we go too much further, Risk Score is the likelihood that you possess some trait. For example, the odds that you are an football fan, or have glaucoma, or might rob a bank ...