Algorithmic Vulnerability: The Hidden Risks of AI in Asset Management
梳理了AI驱动投资系统中的结构性风险，包括数据不稳定、模型幻觉、透明度不足等，指出AI并非消除不确定性，而是重新分配风险，需要新的监管框架。
Artificial intelligence (AI) is rapidly reshaping asset management, expanding the scope of data, increasing model flexibility, and accelerating portfolio decision cycles. While much of the discussion focuses on performance gains and computational efficiency, less attention has been given to the structural risks embedded in AI-driven investment systems. This article develops a taxonomy of AI-related portfolio risks, arguing that AI transforms rather than merely adds to traditional risk categories. Key vulnerabilities include unstable data when conditions change, hallucinations, false signals from extensive model testing, limited transparency in complex and agentic systems, behavioral and governance distortions in human–machine decision making, feedback and model homogeneity that can amplify market stress, and reliance on concentrated computing infrastructure. These risks interact across institutional and systemic layers, potentially creating the illusion of control while embedding latent fragilities. The authors conclude that effective AI adoption requires expanded risk monitoring, adaptation of governance, and awareness of model interdependence across markets. AI does not eliminate uncertainty; it redistributes it in ways that demand new oversight frameworks.