Fairness in Forecast: De-biasing AI-Driven Customer Retention Models Across Demographic Segments in E-Commerce

Abstract

This study investigates fairness in AI-driven customer retention models within the e-commerce sector, with a focus on detecting and mitigating algorithmic bias across demographic segments. As e-commerce platforms increasingly rely on predictive analytics to identify potential churners and optimize marketing strategies, disparities in model performance can inadvertently lead to exclusion or discrimination against certain customer groups, including by age, gender, and region. This paper addresses the critical challenge of demographic bias by evaluating the fairness of several commonly deployed machine learning models for churn prediction, including Logistic Regression, Random Forest, and Gradient Boosting. Using a large-scale, real-world e-commerce dataset enriched with demographic and behavioral features, the study first benchmarks baseline model performance and audits these models for fairness disparities using key metrics such as demographic parity, equal opportunity difference, and disparate impact ratio. The findings reveal significant disparities, particularly along gender and geographic lines, where minority or underrepresented groups experienced higher false-negative rates, potentially leading to missed engagement opportunities. To address these biases, the study applies fairness-enhancing techniques at multiple levels of the machine learning pipeline. These include pre-processing reweighing strategies, in-processing adversarial debiasing, and post-processing equalized odds adjustments. The models were then re-evaluated using both performance and fairness metrics. Results indicate that fairness-aware models maintained competitive predictive accuracy (AUC > 0.80) while substantially reducing fairness gaps, with equal opportunity differences falling by over 60% in some cases. The study concludes that integrating fairness interventions in customer retention modeling is both feasible and effective, providing e-commerce stakeholders with a principled framework to build inclusive, ethical AI systems. These findings contribute to the broader discourse on algorithmic accountability and highlight actionable pathways to ensure that data-driven decision-making does not perpetuate or amplify social inequalities in digital commerce.