Risk Management and Optimization of Artificial Intelligence in E-Commerce Personalization Systems

College of Arts and Sciences, Boston University, Boston, MA 02215, United States

^* Corresponding author: paris616@bu.edu

Abstract

The economic landscape of the 21st century has been rapidly transformed by emerging technologies, particularly artificial intelligence (AI). E-commerce has become the dominant marketplace, offering consumers a highly personalized shopping experience driven by AI-powered recommendation systems. While these systems enhance efficiency and targeting, they also raise significant concerns regarding data privacy, algorithmic biases, and user experience. This paper examines the utilization of AI in e-commerce, with a focus on its impact on personalized recommendation systems. Through case studies, it explores how leading e-commerce platforms integrate AI to optimize consumer engagement. A risk analysis highlights the challenges posed by AI, including privacy breaches, biased recommendations, and the risk of over-personalization. Finally, the paper proposes optimization strategies that balance technological innovation, corporate responsibility, and regulatory intervention to enhance transparency, fairness, and user satisfaction in AI-driven e-commerce platforms. By analyzing the benefits and challenges of AI-powered recommendation systems, this research aims to provide insights into creating a more ethical, efficient, and consumer-friendly e-commerce environment.