DATDAT-001 — Data Quality and Completeness Issues
LLM Failure to Identify Offensive and Insulting Content
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models assessed under SafetyBench demonstrated inconsistent ability to detect offensive content including insults, profanity, and scorn. Organisations deploying LLMs in public-facing services face reputational and regulatory exposure where harmful content goes unidentified or unopposed.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions (Zhang2023) ↗https://airisk.mit.edu/
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