In this paper we introduce and discuss numerical schemes for the approximationof kinetic equations for flocking behavior with phase transitions that incorporate un-certain quantities. This class of schemes here considered make use of a Monte Carloapproach in the phase space coupled with a stochastic Galerkin expansion in the ran-dom space. The proposed methods naturally preserve the positivity of the statisticalmoments of the solution and are capable to achieve high accuracy in the random space.Several tests on a kinetic alignment model with self propulsion validate the proposedmethods both in the homogeneous and inhomogeneous setting, shading light on theinfluence of uncertainties in phase transition phenomena driven by noise such as theirsmoothing and confidence bands.