Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior

We propose estimating Gaussian graphical models (GGMs) that are fair with respect to sensitive nodal attributes. 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 in statistical similarities across nodal groups with different sensitive attributes. Leveraging these metrics, we present Fair GLASSO, a regularized graphical lasso approach to obtain sparse Gaussian precision matrices with unbiased statistical dependencies across groups.