DATDAT-001 — Data Quality and Completeness Issues
LLMs Express Extremist Views and Political Bias in Government Contexts
4/5Sector: GovernmentGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models deployed in government settings have demonstrated extremist outputs and measurable left-leaning political bias across policy domains despite neutrality claims. Departments relying on these tools risk undermining public trust and regulatory compliance where impartiality is a statutory requirement.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023) ↗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.