  We present a new approach to the global fairness verification of tree-based
classifiers. Given a tree-based classifier and a set of sensitive features
potentially leading to discrimination, our analysis synthesizes sufficient
conditions for fairness, expressed as a set of traditional propositional logic
formulas, which are readily understandable by human experts. The verified
fairness guarantees are global, in that the formulas predicate over all the
possible inputs of the classifier, rather than just a few specific test
instances. Our analysis is formally proved both sound and complete.
Experimental results on public datasets show that the analysis is precise,
explainable to human experts and efficient enough for practical adoption.
