Fair Kernel Learning methods for regression and dimensionality reduction built on a previously proposed fair classification framework. The methods rely on the Hilbert-Schmidt independence criterion as the fairness term, which simplifies the problem and allows the inclusion of multiple sensitive variables simultaneously.