DATDAT-001 — Data Quality and Completeness Issues
AI Benchmark Exposes Hate Speech Generation Risk in Language Models
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Language models produce responses that demean individuals based on protected characteristics, revealing systemic gaps in safety alignment. Organisations deploying such models face regulatory exposure and reputational liability without robust pre-deployment hate speech evaluation.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Introducing v0.5 of the AI Safety Benchmark from MLCommons (Vidgen2024) ↗https://airisk.mit.edu/
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