OPSOPS-001 — Monitoring and Alerting Inadequacies
Non-Expert Data Manipulation Corrupts AI Training Pipelines
3/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
AI training data manipulated by staff lacking domain expertise produces corrupted ground truth labels and incompatible data merges, rendering datasets harmful to model development. Boards face operational failures and compliance exposure when data governance does not enforce domain-qualified oversight of data preparation workflows.
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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