Laplace learning is a semi-supervised method that finds missing labels via minimising a Dirichlet energy. It is well known that Laplacian learning is (asymptotically) ill-posed at low labelling rates. In this talk I will identify the bias of Laplace learning and show how this can be corrected leading to significant improvement in performance. The correction in the bias leads one to a Poisson equation. This work is joint with Jeff Calder (University of Minnesota), Brendan Cook (University of Minnesota) and Dejan Slepcev (Carnegie Mellon University).