Fairness Evaluation for Uplift Modeling in the Absence of Ground Truth
This addresses fairness evaluation for uplift modeling in automated decision-making systems, particularly in marketing, but is incremental as it builds on existing methods by introducing surrogate generation.
The paper tackles the problem of evaluating fairness in uplift modeling when ground truth is unavailable, by proposing a framework that generates surrogate labels for counterfactual outcomes, and demonstrates its application in a real-world marketing campaign to enhance fairness assessment.
The acceleration in the adoption of AI-based automated decision-making systems poses a challenge for evaluating the fairness of algorithmic decisions, especially in the absence of ground truth. When designing interventions, uplift modeling is used extensively to identify candidates that are likely to benefit from treatment. However, these models remain particularly susceptible to fairness evaluation due to the lack of ground truth on the outcome measure since a candidate cannot be in both treatment and control simultaneously. In this article, we propose a framework that overcomes the missing ground truth problem by generating surrogates to serve as a proxy for counterfactual labels of uplift modeling campaigns. We then leverage the surrogate ground truth to conduct a more comprehensive binary fairness evaluation. We show how to apply the approach in a comprehensive study from a real-world marketing campaign for promotional offers and demonstrate its enhancement for fairness evaluation.