Type 3: Worse than expected

Mitigation strategy

1. **Implement a Continuous AI Model Robustness and Performance Monitoring Framework** Mandate rigorous stress testing against adversarial and out-of-distribution inputs, coupled with real-time monitoring of deployed systems to identify and flag performance drift, systemic anomalies, and unintended consequences that compromise safety or reliability. 2. **Establish Comprehensive Human Oversight and Accountability Protocols** Define clear human-in-the-loop checkpoints and intervention procedures, ensuring designated personnel possess the requisite competence and authority to safely override or shut down the system when unintended harmful behavior is identified, and maintain detailed logs for retrospective analysis. 3. **Integrate Systemic Risk Assessment into the AI Development Lifecycle** Require pre-deployment risk reports that explicitly analyze potential large-scale, secondary effects (e.g., monoculture, procyclicality, or unpredictable interdependencies) arising from mass adoption, and design technical safeguards to prevent the acceleration of systemic complexity.