Fairness-guided SMT-based Rectification of Decision Trees and Random Forests

TL;DR

This paper introduces FairRepair, a tool that uses SMT solving to modify decision trees and random forests, making them fairer with respect to specified fairness criteria and sensitive attributes, while providing formal guarantees.

Contribution

It presents a novel SMT-based approach for repairing decision trees and random forests to ensure fairness, with a scalable implementation and formal correctness guarantees.

Findings

FairRepair scales to realistic models.

It guarantees soundness and completeness.

Effective bias rectification demonstrated on the adult dataset.

Abstract

Data-driven decision making is gaining prominence with the popularity of various machine learning models. Unfortunately, real-life data used in machine learning training may capture human biases, and as a result the learned models may lead to unfair decision making. In this paper, we provide a solution to this problem for decision trees and random forests. Our approach converts any decision tree or random forest into a fair one with respect to a specific data set, fairness criteria, and sensitive attributes. The \emph{FairRepair} tool, built based on our approach, is inspired by automated program repair techniques for traditional programs. It uses an SMT solver to decide which paths in the decision tree could have their outcomes flipped to improve the fairness of the model. Our experiments on the well-known adult dataset from UC Irvine demonstrate that FairRepair scales to realistic decision trees and random forests. Furthermore, FairRepair provides formal guarantees about soundness and completeness of finding a repair. Since our fairness-guided repair technique repairs decision trees and random forests obtained from a given (unfair) data-set, it can help to identify and rectify biases in decision-making in an organisation.