社交电商平台智能推荐算法的伦理风险与法律规制研究

Research on the Ethical Risks and Legal Regulation of Intelligent Recommendation Algorithms on Social E-Commerce Platforms

摘要: 社交电商平台智能推荐算法在重塑消费生态的同时，衍生出复杂的伦理风险与法律挑战。本文聚焦算法权力扩张下的用户权益保护、市场竞争秩序与平台责任边界失衡问题，通过跨学科视角揭示算法治理的伦理–法律交叉困境。研究首先系统构建智能推荐算法的伦理风险图谱：在个体层面，算法偏见导致歧视性定价与隐私剥削；在社会层面，信息茧房削弱用户自主权，成瘾机制诱发消费异化；在市场层面，流量垄断加剧竞争失序。继而，通过对欧盟《数字服务法》与中国《个人信息保护法》的对比分析，指出当前法律规制面临技术黑箱性导致归责困难、事后监管滞后于算法动态演进、平台自我监管流于形式等现实局限。基于此，本文创新性提出“伦理–法律协同规制”路径：技术治理层面推行算法分级备案与动态影响评估制度，破解可解释性难题；规则重构层面推动从“用户同意”向“平台义务”转向，明确算法推荐的“最小必要原则”与反操纵条款；治理机制层面构建政府监管、第三方审计与用户集体诉讼联动的多元共治体系。研究主张，算法治理需突破传统法律框架，通过技术伦理嵌入与制度创新实现“动态合规”，为平衡技术创新与社会价值提供理论支撑，并为跨境数据流动下的算法协同治理提供前瞻性思考。

Abstract: While reshaping the consumer ecology, the intelligent recommendation algorithm of social e-commerce platforms has derived complex ethical risks and legal challenges. This paper focuses on the protection of user rights and interests, the imbalance between the market competition order and the platform responsibility boundary under the expansion of algorithmic power, and reveals the ethical-legal dilemma of algorithmic governance from an interdisciplinary perspective. Firstly, the ethical risk map of intelligent recommendation algorithm is systematically constructed: at the individual level, algorithmic bias leads to discriminatory pricing and privacy exploitation; at the social level, the information cocoon weakens the autonomy of users, and the addiction mechanism induces consumption alienation. At the market level, traffic monopoly exacerbates the disorder of competition. Then, through the comparative analysis of the EU Digital Services Law and China’s Personal Information Protection Law, it is pointed out that the current legal regulation faces practical limitations such as the difficulty of attributing responsibility due to the black box nature of technology, the lag of post-event supervision behind the dynamic evolution of algorithms, and the mere formality of platform self-regulation. Based on this, this paper innovatively proposes the path of “ethical-legal collaborative regulation”: at the level of technical governance, the system of hierarchical filing and dynamic impact assessment of algorithms is implemented to solve the problem of explainability; at the level of rule reconstruction, it promotes the shift from “user consent” to “platform obligation”, and clarifies the “minimum necessary principle” and anti-manipulation clauses for algorithm recommendation; at the level of governance mechanism, a pluralistic co-governance system is built that links government supervision, third-party audits, and user class action lawsuits. The research argues that algorithm governance needs to break through the traditional legal framework and achieve “dynamic compliance” through technological ethics embedding and institutional innovation, so as to provide theoretical support for balancing technological innovation and social value, and provide forward-looking thinking for algorithmic collaborative governance under cross-border data flow.