  Nowadays Machine Learning (ML) techniques are extensively adopted in many
socially sensitive systems, thus requiring to carefully study the fairness of
the decisions taken by such systems. Many approaches have been proposed to
address and to make sure there is no bias against individuals or specific
groups which might originally come from biased training datasets or algorithm
design. In this regard, we propose a fairness enforcing approach called
EiFFFeL:Enforcing Fairness in Forests by Flipping Leaves which exploits
tree-based or leaf-based post-processing strategies to relabel leaves of
selected decision trees of a given forest. Experimental results show that our
approach achieves a user defined group fairness degree without losing a
significant amount of accuracy.
