Talk: Estimating fair graphs with unbiased connections (Madeline Navarro, Rice)

Title: Estimating fair graphs with unbiased connections

Abstract: We propose estimating graphs that are fair with respect to sensitive nodal attributes. Many real-world models exhibit unfair discriminatory behavior due to biases in data. Such discrimination is known to be exacerbated when data is equipped with pairwise relationships encoded in a graph. We thus propose an optimization-based approach to accurately infer graphs while discouraging biased edges. To this end, we present bias metrics that measure topological demographic parity to be applied as convex penalties, suitable for most optimization-based graph learning methods. We also propose an efficient proximal gradient algorithm to obtain the estimates. Theoretically, we express the tradeoff between fair and accurate estimated graphs. Critically, this includes demonstrating when accuracy can be preserved in the presence of a fairness regularizer. Our empirical validation includes synthetic and real-world simulations that illustrate the value and effectiveness of our proposed optimization problem and iterative algorithm.