DATDAT-001 — Data Quality and Completeness Issues
LLMs Generating Harmful Content Targeting Children and Young People
5/5Sector: LegalGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models can be manipulated to produce content that is harmful to minors, a failure category treated as legally and morally distinct from general unlawful conduct. Boards face heightened regulatory exposure and reputational risk where deployed systems lack specific safeguards for child protection obligations.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024) ↗https://airisk.mit.edu/
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