Abstract
"Double, double toil and trouble; Fire burn and cauldron bubble." As Shakespeare's witches foretold chaos through cryptic prophecies, modern capital markets grapple with systemic risks concealed by opaque AI systems. According to the IMF, the August 5, 2024, plunge in Japanese and U.S. equities can be linked to algorithmic trading, yet absent from the existing AI incidents database, exemplifies this transparency crisis. Current AI incident databases, reliant on crowdsourcing or news scraping, systematically overlook capital market anomalies, particularly in algorithmic and high-frequency trading. We address this critical gap by proposing a regulatory-grade global database that synthesises post-trade reporting frameworks with proven incident documentation models from healthcare and aviation. Our framework's temporal data omission technique masks timestamps while preserving percentage-based metrics, enabling sophisticated cross-jurisdictional analysis of emerging risks while safeguarding confidential business information. Synthetic data validation (modelled after real life published incidents) (n=2,999 incidents) reveals compelling patterns: systemic risks transcending geographical boundaries, market manipulation clusters distinctly identifiable via K-means algorithms, and AI system typology exerting significantly greater influence on trading behaviour than geographical location, This tripartite solution empowers regulators with unprecedented cross-jurisdictional oversight, financial institutions with seamless compliance integration, and investors with critical visibility into previously obscured AI-driven vulnerabilities. We call for immediate action to strengthen risk management and foster resilience in AI-driven financial markets against the volatile "cauldron" of AI-driven systemic risks, promoting global financial stability through enhanced transparency and coordinated oversight.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Eighth AAAI/ACM Conference on AI, Ethics, and Society (AIES-25): Main Track II |
| Editors | Emanuelle BURTON, Nicholas MATTEI, Andrés PÁEZ |
| Publisher | AAAI press |
| Pages | 1181-1193 |
| Number of pages | 13 |
| ISBN (Print) | 9781577359029 |
| DOIs | |
| Publication status | Published - 15 Oct 2025 |
| Event | The 8th AAAI/ACM Conference on AI, Ethics, and Society - Madrid, Spain Duration: 20 Oct 2025 → 22 Oct 2025 |
Publication series
| Name | Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society |
|---|---|
| Publisher | Association for the Advancement of Artificial Intelligence |
| Number | 2 |
| Volume | 8 |
Conference
| Conference | The 8th AAAI/ACM Conference on AI, Ethics, and Society |
|---|---|
| Abbreviated title | AIES-25 |
| Country/Territory | Spain |
| City | Madrid |
| Period | 20/10/25 → 22/10/25 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 10 Reduced Inequalities
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