Risks from Malfunctions

Mitigation strategy

1. Implement robust technical safeguards, including comprehensive adversarial testing (red-teaming) and continuous validation frameworks, to enhance model robustness against unexpected failures and preserve product functionality post-deployment. 2. Establish real-time telemetry and continuous behavioral monitoring systems to detect and prevent performance degradation, model drift/decay, and subtle anomalies, enabling automated remediation or prompt human-in-the-loop intervention. 3. Mandate stringent data governance and auditing protocols across the entire AI lifecycle—from acquisition to deployment—to proactively identify and mitigate algorithmic bias through techniques such as fairness metrics, re-weighting, and re-sampling of training datasets.