Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics
This work investigates the problem of graph fairness without demographic information and proposes Grad2Fair, a gradient-guided approach for group fairness without demographics that directly leverages gradients to debias and eliminates demographic prediction, thereby enabling stable fairness performance.