Learning Time-Varying Convexifications of Multiple Fairness Measures
This addresses the challenge of balancing multiple fairness notions in machine learning models, which is incremental as it builds on existing fairness regularization approaches.
The paper tackles the problem of learning time-varying convex combinations of multiple fairness measures with limited graph-structured feedback, and proposes a method to adaptively adjust fairness regularizer weights on the fly.
There is an increasing appreciation that one may need to consider multiple measures of fairness, e.g., considering multiple group and individual fairness notions. The relative weights of the fairness regularisers are a priori unknown, may be time varying, and need to be learned on the fly. We consider the learning of time-varying convexifications of multiple fairness measures with limited graph-structured feedback.