DATDAT-001 — Data Quality and Completeness Issues
LLM Systems Generating Biased, Toxic and Privacy-Violating Output
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models produce outputs containing bias, toxic language, and private information, representing a systematic content risk rather than isolated failure. Organisations deploying these systems face regulatory exposure and reputational liability without robust output monitoring and content governance controls.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024) ↗https://airisk.mit.edu/
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