Abstract

Customer churn prediction has become a central analytic capability in subscription-based and relationship-intensive industries, yet prevailing machine learning approaches prioritize short-term predictive accuracy while neglecting fairness, transparency, and long-term value creation (Barocas et al., 2019; Davenport et al., 2020). This imbalance creates strategic and ethical risks, including discriminatory targeting, erosion of customer trust, and misaligned retention investments (Kleinberg et al., 2017; Mitchell et al., 2019). Drawing on theories of algorithmic governance, customer equity, and socio-technical systems, this study develops and evaluates a Responsible AI framework for churn prediction that integrates fairness constraints, interpretable learning, and long-term value optimization (Doshi-Velez & Kim, 2017; Lemon & Verhoef, 2016).
The research addresses a gap in the churn literature, which remains largely silent on how responsible AI principles can be operationalized without degrading economic performance (Mullainathan & Spiess, 2017). Using a large-scale customer dataset from a multi-channel service provider, we compare conventional black-box churn models with responsibility-aware alternatives that incorporate group fairness metrics, local and global explainability, and dynamic value-based objectives (Lundberg & Lee, 2017; Zhang et al., 2018). Results demonstrate that responsible models achieve statistically comparable predictive accuracy while significantly reducing demographic bias and improving retention return on investment over multiple periods, consistent with findings from applied analytics in healthcare and infrastructure systems (Hasan et al., 2021; Rasel et al., 2022). Mechanism-level analysis reveals that transparency reshapes managerial intervention strategies, shifting focus from reactive discounting to capability-based retention (Ransbotham et al., 2020; Shah et al., 2025).