AUTHOR=Yan Siwen , Odom Phillip , Pasunuri Rahul , Kersting Kristian , Natarajan Sriraam TITLE=Learning with privileged and sensitive information: a gradient-boosting approach JOURNAL=Frontiers in Artificial Intelligence VOLUME=6 YEAR=2023 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1260583 DOI=10.3389/frai.2023.1260583 ISSN=2624-8212 ABSTRACT=

We consider the problem of learning with sensitive features under the privileged information setting where the goal is to learn a classifier that uses features not available (or too sensitive to collect) at test/deployment time to learn a better model at training time. We focus on tree-based learners, specifically gradient-boosted decision trees for learning with privileged information. Our methods use privileged features as knowledge to guide the algorithm when learning from fully observed (usable) features. We derive the theory, empirically validate the effectiveness of our algorithms, and verify them on standard fairness metrics.