Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior

arXiv:2406.09513v1 Announce Type: cross
Abstract: We propose estimating Gaussian graphical models (GGMs) 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. Additionally, the effect of biased data on graphical models is largely underexplored. We thus introduce fairness for graphical models in the form of two bias metrics to promote balance …

abstract arxiv attributes behavior biases cs.lg data discrimination eess.sp fair relationships statistical stat.ml type unbiased world world models