Local Statistical Parity for the Estimation of Fair Decision Trees
This work addresses fairness in algorithmic decision-making, but it is incremental as it modifies existing tree estimation methods with local constraints.
The paper tackled the problem of promoting fairness in decision tree estimation by proposing a local fairness criterion related to Statistical Parity and incorporating it into recursive algorithms. The results showed that their C-LRT algorithm successfully balanced accuracy and fairness on standard datasets.
Given the high computational complexity of decision tree estimation, classical methods construct a tree by adding one node at a time in a recursive way. To facilitate promoting fairness, we propose a fairness criterion local to the tree nodes. We prove how it is related to the Statistical Parity criterion, popular in the Algorithmic Fairness literature, and show how to incorporate it into standard recursive tree estimation algorithms. We present a tree estimation algorithm called Constrained Logistic Regression Tree (C-LRT), which is a modification of the standard CART algorithm using locally linear classifiers and imposing restrictions as done in Constrained Logistic Regression. Finally, we evaluate the performance of trees estimated with C-LRT on datasets commonly used in the Algorithmic Fairness literature, using various classification and fairness metrics. The results confirm that C-LRT successfully allows to control and balance accuracy and fairness.