HUMHUM-003 — Human-AI Collaboration Design Flaws
Large Language Models Fabricate Confident but False Outputs
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models generate plausible yet factually wrong or nonsensical content with apparent certainty, a behaviour known as hallucination. Boards relying on LLM outputs without verification controls face material risks of misinformed decisions and reputational harm.
Domain
Human Factors
Blindspots in change management, skills, human-AI collaboration, trust, workforce, and culture.
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.