HUMHUM-003 — Human-AI Collaboration Design Flaws
Noisy Training Data Causes LLM Hallucinations at Scale
4/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
Large language models trained on massive corpora absorb misinformation and noise, embedding factual errors directly into model parameters. Organisations deploying such models face systemic accuracy risks that cannot be resolved through post-deployment safeguards alone.
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/
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