Algorithmic Bias in AI Marketing: Implications for Consumer Equity and Brand Reputation

Abstract

Artificial intelligence (AI) is increasingly embedded in targeting, personalization, pricing, recommendation, and content-generation systems, intensifying concerns about algorithmic bias and its consequences for consumers and brands. This critical literature review synthesizes peer-reviewed and applied research published primarily between 2017 and 2025 to examine how bias enters AI-enabled marketing through data, model design, optimization objectives, and deployment contexts. The synthesis identifies three recurring mechanisms data, model, and contextual bias and shows how they can produce representational, measurement, and evaluation disparities. Across the reviewed literature, these disparities are associated with discriminatory targeting, exclusion, opaque personalization, perceived unfairness, and erosion of consumer trust, with downstream risks for brand legitimacy and reputation. The review further integrates technical, organizational, and regulatory mitigation approaches, including data audits, fairness-aware modeling, explainable AI, human oversight, governance structures, impact assessment, and regulatory compliance. The article contributes an integrated socio-technical perspective that connects the AI lifecycle to consumer-equity outcomes and reputational consequences. It concludes that responsible AI marketing requires fairness and accountability to be treated as design and governance objectives rather than post-hoc corrections.