Who Governs the Algorithm? Accountability, Liability, and Consumer Protection in AI Marketing

Abstract

The integration of artificial intelligence into marketing practice has created a structural governance problem that existing scholarly frameworks have not adequately addressed. When AI-driven marketing systems produce discriminatory profiling, manipulative personalization, or opaque dynamic pricing, civil liability law cannot assign responsibility, organizations lack the governance infrastructure to prevent or remediate harm, and human resource management systems fail to create substantive oversight capacity. This paper conceptualizes this condition as the AI Marketing Accountability Gap and argues that it arises from four structurally interdependent governance failures spanning the legal, organizational, human resource management, and marketing ethics dimensions of AI marketing practice. To address these failures, the paper develops the AI Marketing Accountability Model (AMAM), an original theoretical framework that integrates Calabresi's cheapest-cost-avoider principle, Jensen and Meckling's principal-agent theory, Floridi et al.'s duty-of-care framework, and the marketing ethics consumer-autonomy tradition, unified by the Concentration Principle. The framework is applied through comparative legal analysis of governance systems in the EU, US, UK, and Estonia, and through systematic legal-doctrinal gap analysis of Georgian law, examined as a representative case of EU-aspiring emerging economies that are rapidly digitalizing without adequate regulatory infrastructure. The analysis generates five research propositions and a six-phase policy agenda. Three contributions are made: the AMAM, the first integrated governance framework addressing all four accountability dimensions; the first systematic AI marketing governance analysis in Georgia; and a transferable governance model for developing economies.