Abuse of Algorithmic Rights: The Boundary of Criminal Liability for AI Manipulation in the Securities Market - Zhang, Yuhan (International Journal of Social Sciences and Public Administration, 2026)
With the deepening application of AI in securities markets, the issue of abuse of power in algorithmic trading has become prominent, posing a threat to market fairness and investor rights. This paper focuses on the challenges in identifying criminal liability for algorithmic market manipulation and explores the scope of liability for AI decision-making entities under the current legal framework. Existing research primarily revolves around algorithmic transparency, determination of subjective intent, and assessment of market impact, but a consensus has not yet been formed regarding liability attribution for autonomous algorithmic decision-making. The "black box" nature of intelligent algorithms challenges the determination of traditional criminal law subjective elements, while novel manipulation methods expose regulatory lags. Significant differences in management across jurisdictions reflect conflicts between technological ethics and legal values. Current research lacks targeted liability theories, suffers from insufficient interdisciplinary integration, and has a scarcity of empirical samples. The future requires the construction of a criminal liability framework adapted to the characteristics of algorithms, improving diversified regulatory models, providing legal theoretical support for balancing technological innovation and market order, and contributing to the improvement of China’s securities market regulatory system.