  Ensuring that a predictor is not biased against a sensible feature is the key
of Fairness learning. Conversely, Global Sensitivity Analysis is used in
numerous contexts to monitor the influence of any feature on an output
variable. We reconcile these two domains by showing how Fairness can be seen as
a special framework of Global Sensitivity Analysis and how various usual
indicators are common between these two fields. We also present new Global
Sensitivity Analysis indices, as well as rates of convergence, that are useful
as fairness proxies.
