Towards Ethical AI-driven Marketing: A Framework for Explainability, Consumer Empowerment and Algorithmic Accountability in Emerging Markets

Abstract

The diffusion of artificial intelligence into marketing practice has brought with it a fundamental governance problem: The most predictively capable AI systems are, typically, the least interpretable. This opacity creates information asymmetries between firms and consumers and complicates the ethical legitimacy of automated marketing decisions. This article argues that explainable AI (XAI) constitutes a necessary, and currently undertheorised, layer in the design of consumer-facing AI systems. Drawing on consumer trust theory, algorithmic decision-making research and marketing ethics literature, we develop the explainable marketing AI (XMAI) framework: a theoretical model proposing four interdependent components—algorithmic transparency, contextual relevance, consumer empowerment and ethical accountability—that jointly govern the deployment of explainable AI systems in marketing contexts. Four propositions link these components to consumer trust, perceived fairness, long-term engagement and consumer well-being. The framework is developed in the context of India and comparable developing economies, where rapid AI adoption in marketing is outpacing governance infrastructure, and where consumer digital literacy and institutional trust present conditions that differ from the Western settings in which most AI ethics frameworks have been formulated. The article contributes a theoretical foundation for researchers, practitioners and policymakers to close the gap between AI capability and accountability in marketing.