[2603.04259] When AI Fails, What Works? A Data-Driven Taxonomy of Real-World AI Risk Mitigation Strategies

Computer Science > Computers and Society arXiv:2603.04259 (cs) [Submitted on 4 Mar 2026] Title:When AI Fails, What Works? A Data-Driven Taxonomy of Real-World AI Risk Mitigation Strategies Authors:Evgenija Popchanovska, Ana Gjorgjevikj, Maryan Rizinski, Lubomir Chitkushev, Irena Vodenska, Dimitar Trajanov View a PDF of the paper titled When AI Fails, What Works? A Data-Driven Taxonomy of Real-World AI Risk Mitigation Strategies, by Evgenija Popchanovska and 5 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) are increasingly embedded in high-stakes workflows, where failures propagate beyond isolated model errors into systemic breakdowns that can lead to legal exposure, reputational damage, and material financial losses. Building on this shift from model-centric risks to end-to-end system vulnerabilities, we analyze real-world AI incident reporting and mitigation actions to derive an empirically grounded taxonomy that links failure dynamics to actionable interventions. Using a unified corpus of 9,705 media-reported AI incident articles, we extract explicit mitigation actions from 6,893 texts via structured prompting and then systematically classify responses to extend MIT's AI Risk Mitigation Taxonomy. Our taxonomy introduces four new mitigation categories, including 1) Corrective and Restrictive Actions, 2) Legal/Regulatory and Enforcement Actions, 3) Financial, Economic, and Market Controls, and 4) Avoidance and Denial, capturing response pa...