HUMHUM-003 — Human-AI Collaboration Design Flaws
LLM Outputs Contradict Source Material Due to Faithfulness Errors
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models generate content that misrepresents or contradicts the source material provided, producing factually inaccurate outputs. Organisations relying on LLM-assisted workflows risk reputational, legal, and operational harm from unchecked hallucination in high-stakes decisions.
Domain
Human Factors
Blindspots in change management, skills, human-AI collaboration, trust, workforce, and culture.
Source
MIT AI Risk Repository — Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024) ↗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.