Study on the Risk Transmission Mechanism of AI Algorithmic Black Box in Live-Streaming E-Commerce Supply Chain Finance

本研究聚焦AI算法对直播电商供应链金融风险的传导机制，揭示了算法不可解释性引发的三种核心问题：风险控制失效、信息不对称加剧以及系统性风险累积。通过机制分析后发现，算法黑箱通过模糊决策逻辑、加剧数据垄断、放大目标冲突等途径，促使风险从局部传导至全局，形成网络化的系统性金融风险威胁。针对上述问题，研究提出系统性治理框架，包括构建可解释性算法、重构数据治理结构、实施跨平台协同监管，以增强透明度、优化验证机制并阻断风险传播。然而，研究依旧存在局限：治理策略的实证效果尚未得到验证，算法异质性对治理普适性的影响并未充分探讨，风险动态性与非线性的特征的模拟分析不足。未来需结合案例模拟与跨学科方法深化研究，为直播电商平台供应链金融的智能化治理提供更为全面的理论与实践支持。This study focuses on the risk transmission mechanism of AI algorithms in live-streaming e-commerce supply chain finance, revealing three core issues triggered by algorithmic unexplainability: risk control failure, exacerbated information asymmetry, and systemic risk accumulation. Through mechanism analysis, it is found that algorithmic black boxes propagate risks from localized to systemic levels through pathways such as obfuscation of decision logic, intensified data monopolization, and amplification of goal conflicts, forming networked systemic financial risk threats. To address these issues, the research proposes a systemic governance framework, including building explainable algorithms, restructuring data governance frameworks, and implementing cross-platform collaborative supervision, to enhance transparency, optimize verification mechanisms, and block risk transmission. However, limitations persist: empirical validation of governance strategies remains untested, the impact of algorithmic heterogeneity on governance universality is not fully explored, and simulation analysis of risk dynamics and nonlinearity is insufficient. Future research should integrate case simulations and interdisciplinary methods to deepen investigations, providing more comprehensive theoretical and practical support for intelligent governance in live-streaming e-commerce supply chain finance.