DATDAT-001 — Data Quality and Completeness Issues
LLMs Fail to Reliably Reflect Social Norms or Maintain Neutrality on Contested Values
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models inconsistently apply social norms, oscillating between offensive outputs and inappropriate value promotion on contested topics. Boards face reputational and regulatory exposure where deployed systems cannot demonstrate consistent, auditable neutrality.
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/
Could this happen in your organisation?
A Velinor AI Audit maps your active AI portfolio against the 50+ blindspots and benchmarks against documented sector failures like this one. A board-ready foresight document in 5 weeks.