Organizational and Social Impact of AI-Driven Fake Review Detection in E-commerce

Abstract

The rapid growth of e-commerce and mobile transactions has fueled an increase in fraudulent activities with fake online reviews posing a major threat to businesses and consumers. Such reviews, often generated by seller, third parties, or bots, distort reputations, mislead consumer decisions, and erode trust. To address this, organizations are adopting AI-driven detection systems that offer scalability and efficiency while also raising challenges around bias, transparency, and ethical oversight. Drawing on insights from a doctoral dissertation focused on real-time detection of fake reviews using AI, this article examines how these systems shape platform governance, seller behavior, and consumer perceptions of fairness and trust. It highlights implications for risk management, compliance, and regulation, while also assessing social consequences such as false positives, opacity, and perceived bias. The paper recommends strengthening platform governance, ensuring fair treatment of sellers, and enhancing internal risk management at the organizational level, while at the social level, emphasizing consumer trust-building, fairness and legitimacy, and embedding ethical safeguards to protect privacy, accountability, and literacy. Finally, this paper calls for a responsible, human-centric approach to AI deployment that balances automation needs with ethical oversight, enhances consumer confidence, improves platform integrity, and promotes organizational efficiency.