  Group fairness is an important concern for machine learning researchers,
developers, and regulators. However, the strictness to which models must be
constrained to be considered fair is still under debate. The focus of this work
is on constraining the expected outcome of subpopulations in kernel regression
and, in particular, decision tree regression, with application to random
forests, boosted trees and other ensemble models. While individual constraints
were previously addressed, this work addresses concerns about incorporating
multiple constraints simultaneously. The proposed solution does not affect the
order of computational or memory complexity of the decision trees and is easily
integrated into models post training.
