Gradient Institute has written a paper that extends the work we submitted to the 2020 Ethics of Data Science Conference on fair regression in a number of ways. First, 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. The paper also adapts 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. Read the draft paper on arxiv.
Fast methods for fair regression
25 February 2020
