Ethical Intelligence in Digital Marketing: The Interplay of Algorithmic Bias, Transparency, and Data Privacy on Consumer Trust

Abstract

The aims of this research are to analyze the influence of ethical intelligence (algorithmic bias, transparency within AI systems, and data privacy) on consumer trust via perceived fairness in AI-embedded digital marketing environments. Data were collected from 385 active digital consumers using a quantitative cross-sectional research design and analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicated that data privacy apprehension (β = 0.572, p < 0.05). In addition, fairness is a significant predictor of customer trust (β = 0.869, p levels which serve as the main psychological mediating variable between ethical system design and relational confidence. The model possesses strong explanatory power (fairness: R² = 0.728; trust: R² = 0.756), hence ethical transparency and accountability as data steward matters for trust. The paper extends the FAT (Fairness–Accountability–Transparency) model by demonstrating that AI ethics in marketing is an empirically informed issue and contends that ethical intelligence stands as a strategic imperative for sustainable digital trust and consumer engagement in the age of intelligent automation.