A Convex Framework for Fair Regression
This work addresses fairness concerns in machine learning for regression tasks, but it is incremental as it builds on existing fairness notions with a new optimization framework.
The authors tackled the problem of balancing accuracy and fairness in regression by introducing a family of convex fairness regularizers, enabling efficient optimization and trade-off analysis across six datasets, with results quantified by a 'Price of Fairness' metric.
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the rang from notions of group fairness to strong individual fairness. By varying the weight on the fairness regularizer, we can compute the efficient frontier of the accuracy-fairness trade-off on any given dataset, and we measure the severity of this trade-off via a numerical quantity we call the Price of Fairness (PoF). The centerpiece of our results is an extensive comparative study of the PoF across six different datasets in which fairness is a primary consideration.