HUMHUM-003 — Human-AI Collaboration Design Flaws
AI Model Generates Factually Inaccurate Outputs Through Hallucination
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models produce confident but false content ungrounded in training data or user inputs. Boards deploying AI in any client-facing or decision-critical context face direct liability and reputational exposure from unchecked hallucination.
Domain
Human Factors
Blindspots in change management, skills, human-AI collaboration, trust, workforce, and culture.
Source
MIT AI Risk Repository — AI Risk Atlas (IBM2025) ↗https://airisk.mit.edu/
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