Year: 2022
Type: conference-paper
Abstract: Decision trees are increasingly used to make socially sensitive decisions, where they are expected to be both accurate and fair, but it remains a challenging task to optimize the learning algorithm for fairness in a predictable and explainable fashion. To overcome the challenge, we propose an iterative framework for choosing decision attributes, or features, at each level by formulating feature selection as a series of mixed integer optimization problems. Both fairness and accuracy requirements are encoded as numerical constraints and solved by an off-the-shelf constraint solver. As a result, the trade-off between fairness and accuracy is quantifiable. At a high level, our method can be viewed as a generalization of the entropy-based greedy search techniques such as and , and existing fair learning techniques such as and . Our experimental evaluation on six datasets, for which demographic parity is used as the fairness metric, shows that the method is significantly more effective in reducing bias than other methods while maintaining accuracy. Furthermore, compared to non-iterative constraint solving, our iterative approach is at least 10 times faster.
Authors: Jingbo Wang, Yannan Li, Chao Wang
Institution: University of Southern California
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: hybrid
Estimated APC paid: $5,392
Funder: National Science Foundation
OpenAlex ID: W4289874389