DATDAT-001 — Data Quality and Completeness Issues
Context-Dependent AI Harm Categories Pose Deployment Governance Risk
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
AI models may produce sexual content or unvetted specialist advice that is benign in one deployment context yet harmful in another, such as child-facing applications. Boards must ensure governance frameworks mandate context-specific hazard assessments before each deployment rather than relying on generic model-level safety clearances.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — AILUMINATE: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons (Ghosh2024) ↗https://airisk.mit.edu/
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