Responsible Uplift Modeling

Automated intervention policies have become highly prevalent within firms, with "algorithmic personalization" techniques at their foundation. These methods leverage individual-level data to decide which groups should be targeted by the firm's policies. While such policies are naturally guided by the multi-dimensional heterogeneity that exists among individuals, relying on some dimensions of such heterogeneity may unintentionally result in biased outcomes for socially-disadvantaged groups. This work focuses on a particular form of personalization: Uplift Modeling. While research on fairness in algorithmic personalization has been growing in recent years, the broader societal impact of Uplift Modeling has largely been overlooked in previous technical work. We introduce the first in-processing, learning-based method for Fair Uplift Modeling, applicable in both static and dynamic environments. Our Uplift Models are evaluated on real-world datasets, demonstrating promising results.