Intersectionality: Multiple Group Fairness in Expectation Constraints
This work addresses fairness concerns for machine learning practitioners by enabling multiple constraints in tree-based models, though it is incremental as it builds on prior single-constraint methods.
The paper tackles the problem of enforcing multiple group fairness constraints simultaneously in decision tree regression and ensemble models, achieving a solution that maintains computational and memory complexity without retraining.
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.