AI Hallucination refers to instances where AI systems, particularly large language models, generate information that is factually incorrect, fabricated, or inconsistent with reality, while presenting it with apparent confidence. The term reflects the AI's tendency to "see" patterns and connections that don't actually exist.
Types of AI hallucinations include: factual hallucinations (stating incorrect facts with confidence), fabricated references (citing non-existent papers, statistics, or sources), logical inconsistencies (contradicting previously stated information), entity hallucinations (attributing actions or quotes to wrong people/organizations), and temporal hallucinations (incorrect dates or timelines).
The enterprise impact of AI hallucinations is significant: incorrect information in business reports, fabricated legal citations (as seen in publicized court cases), inaccurate financial analysis, false claims in customer communications, incorrect code that introduces bugs or vulnerabilities, and misleading research summaries.
Mitigation strategies include: mandatory human review of all AI-generated content, fact-checking workflows before publication, grounding AI outputs in verified data sources (RAG), training employees to critically evaluate AI responses, using multiple AI models for cross-validation, and implementing organizational policies requiring AI output verification.
