DATDAT-001 — Data Quality and Completeness Issues
Poisoned or Unlawful Training Data Corrupts Legal AI Output
3/5Sector: LegalGeography: GlobalStage: DevelopIngested: —
Executive Summary
Legal AI systems trained on biased, IPR-infringing, or adversarially poisoned data produce unreliable and potentially unlawful outputs. Boards face liability exposure and regulatory censure if data provenance and integrity controls are absent from AI governance frameworks.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — AI Safety Governance Framework (TC2602024) ↗https://airisk.mit.edu/
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