Artificial Intelligence and Behavioral Engineering: Hidden Societal Costs and Design Challenges in Consumer Contexts

Artificial Intelligence (AI) is no longer just optimizing consumer experiences—it is actively engineering behavioral environments through algorithmic design. This qualitative exploratory analysis examines the hidden societal costs and design challenges of AI-driven consumer systems by synthesizing insights from psychology, digital ethics, behavioral economics, and human–AI interaction. The study identifies ten interrelated domains—such as hyper-personalization, emotional AI, dark nudging, neuromarketing, and synthetic influencers—that reveal how AI systems reshape consumer autonomy, trust, and decision-making. A novel conceptual framework and Theme–Implication Matrix are developed to link AI’s behavioral mechanisms with design-level ethical risks and governance needs. Findings emphasize the need for transparent, human-centered AI architectures that balance personalization with cognitive integrity. The study contributes to socio-technical system design by framing AI as a behavioral engineering force with profound implications for consumer agency, digital equity, and sustainable innovation.