Fast methods for fair regression

Feb 25, 2020
Technical audience

This paper extends the work submitted by Gradient to the 2nd Ethics of Data Science Conference on fair regression in a number of ways. Firstly, the methods introduced in the earlier paper for quantifying the fairness of continuous decisions are benchmarked against “gold standard” (but typically intractable) techniques in order to test their efficacy. Next we adapt these methods to produce a fast and scalable algorithm for adjusting the predictions of regression models to increase the fairness of their outcomes for a multitude of fairness criteria. You can find the draft paper on arxiv.