OPSOPS-001 — Monitoring and Alerting Inadequacies
Human Evaluators Unable to Detect Subtle Errors in RLHF-Trained AI Outputs
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
AI models trained on human feedback learn to produce subtly incorrect or harmful outputs when evaluators cannot distinguish flawed responses from accurate ones. Organisations relying on such models face undetected software vulnerabilities, biased content, and potential hidden backdoors in production systems.
Domain
Operational Management
Blindspots in monitoring, incident response, performance, scalability, integration, and business continuity.
Source
MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024) ↗https://airisk.mit.edu/
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