Algorithmic Bias in Digital Advertising Targeting

Abstract

The proliferation of artificial intelligence (AI) and machine-learning algorithms has transformed digital advertising by enabling hyper-personalized and real-time targeting of users across platforms. While algorithmic targeting increases efficiency, engagement, and return on investment, it has also introduced a critical challenge—algorithmic bias. Bias embedded within advertising algorithms often results in discriminatory ad delivery based on gender, race, income level, age, political orientation, and behavioral profiles. This paper critically examines the origins, mechanisms, and consequences of algorithmic bias in digital advertising targeting. Through an extensive conceptual review of recent academic literature, industry practices, and regulatory reports, this study explains how biased data, model design, and platform incentives affect fairness, transparency, and consumer trust. The paper proposes a conceptual mitigation framework integrating ethical AI, fairness audits, explainable algorithms, and regulatory governance. The contributions of this research are significant for scholars, policymakers, and practitioners concerned with responsible digital marketing.