  We consider fair network topology inference from nodal observations.
Real-world networks often exhibit biased connections based on sensitive nodal
attributes. Hence, different subpopulations of nodes may not share or receive
information equitably. We thus propose an optimization-based approach to
accurately infer networks 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. Moreover, we encourage equitable treatment for any number of
subpopulations of differing sizes. We validate our method on synthetic and
real-world simulations using networks with both biased and unbiased
connections.
