DATDAT-001 — Data Quality and Completeness Issues
LLMs Generating Toxic and Identity-Attacking Language Toward Users
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models trained on internet data reproduce offensive slurs and identity-based attacks targeting users by culture, race, and gender. Organisations deploying such models face reputational, legal, and regulatory exposure if harmful outputs reach end users.
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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