A Framework for Evaluating Empathy in Generative AI Customer Service

Abstract

This paper proposes a structured framework for evaluating empathy in Generative AI (GenAI) customer-service systems, addressing a critical gap in how human-centric qualities are assessed in automated interactions. Traditional service-quality models, such as SERVQUAL and the Interpersonal Reactivity Index, conceptualize empathy through human judgment and emotional capability—dimensions that do not directly transfer to GenAI. To adapt these constructs, the paper introduces a dual-constraint model in which emotional alignment and policy compliance jointly determine a bounded form of empathy suitable for AI-mediated service. The study employs conceptual simulation, integrating psychological theory, service-quality research, and responsible-AI standards including EmotionML, IEEE 7010, and ISO/IEC 23894. Symbolic modelling and unit-free visualization are used to illustrate how empathy, factual integrity, and customer outcomes interact across feasible and infeasible regions. Governance metrics—such as Bounded Empathy Drift, Policy Breach Rate, and Insensitive Response Rate—demonstrate how organizations can monitor empathic behaviour during deployment and ensure compliance with ethical constraints. The framework contributes a measurable, governable conceptualization of empathy for GenAI services, offering a foundation for empirical validation and practical implementation. It positions empathy as a strategic performance dimension influencing satisfaction, trust, and service experience within AI-enabled customer ecosystems.