AI驱动的电商个性化与伦理边界：算法透明度与数据隐私的平衡机制研究

The Balance Mechanism between Algorithmic Transparency and Data Privacy in AI-Driven E-Commerce Personalization: An Ethical Boundary Perspective

摘要: 随着人工智能技术在电子商务领域的深度渗透，个性化推荐系统已成为平台提升用户体验与商业转化率的核心工具。然而，算法透明度与数据隐私保护之间的结构性张力构成了当前数字商业生态面临的重大伦理挑战。本文基于隐私计算、可解释人工智能以及数据保护法律框架的理论视角，系统分析了个性化服务中透明度与隐私的博弈关系，探讨了差分隐私、联邦学习等隐私增强技术在推荐系统中的应用机制，并提出了“分层透明”与“动态同意”相结合的技术–制度协同治理框架。研究发现，算法透明度与数据隐私并非零和博弈，通过隐私保护下的可解释推荐设计，可以在保障用户隐私权益的同时实现适度的算法透明，从而构建可持续的人机信任关系。

Abstract: With the deep penetration of artificial intelligence technology into the field of e-commerce, personalized recommendation systems have become a core tool for platforms to improve user experience and business conversion rates. However, the structural tension between algorithm transparency and data privacy protection has become a major ethical challenge facing the current digital business ecosystem. Based on the theoretical perspectives of privacy computing, explainable artificial intelligence, and data protection legal frameworks, this paper systematically analyzes the game relationship between transparency and privacy in personalized services, explores the application mechanisms of privacy-enhancing technologies such as differential privacy and federated learning in recommendation systems, and proposes a coordinated governance framework that combines “layered transparency” with “dynamic consent” in both technology and institutions. The study finds that algorithm transparency and data privacy are not a zero-sum game. Through the design of explainable recommendations under privacy protection, it is possible to achieve a proper level of algorithm transparency while protecting users’ privacy rights, thereby building a sustainable relationship of human-machine trust.