In this paper,we consider a setting where sensitive attributes indirectly manifest in an auxiliary representation graphrather than being directly observed.

Theuseofmetric elicitation enables a practitioner to tune the performance and fairness metrics to the task, context,andpopulationathand.

Constructing a directed cyclic graph (DCG) is challenged by both algorithmic difficulty and computational burden. Comparing multiple DCGs is even more difficult, compounded by the need to identify dynamic causalities across graphs.

We also establish a global superlinear convergence rate ofO((1t)t).