Hallucination
When an AI model generates information that sounds plausible but is factually incorrect or entirely fabricated. Common with statistics, citations, and historical claims.
Why it matters
This is the single biggest reason not to trust AI blindly. A model can invent a court case, a research paper, a quote, or a statistic and present it in perfectly confident, professional language. People have gotten into real trouble by pasting fabricated citations into legal filings or reports. Knowing hallucinations happen turns you into a careful user who verifies anything that actually matters.
In practice
Ask an AI for "three studies proving X" and it may hand you three official-sounding titles, authors, and journal names that don't exist. The wording looks flawless, which is exactly the trap. Before you rely on any fact, name, number, or link an AI gives you, check it against a real source. Treat AI output as a confident first draft, never as a verified reference.
Related terms
AI Alignment
The research challenge of ensuring AI systems pursue goals that are beneficial to humans. Misaligned AI could technically achieve its objective while causing unintended harm. Alignment research aims to make AI reliably helpful, harmless, and honest.
Bias (in AI)
Systematic errors in AI outputs reflecting prejudices in training data. Can manifest as gender stereotyping, racial assumptions, or cultural insensitivity in generated content.
Constitutional AI
A training approach where AI models are given a set of principles (a 'constitution') and learn to self-critique and revise their outputs to comply with those principles. Reduces reliance on human feedback for safety alignment.
Data Poisoning
A security attack where malicious data is deliberately introduced into AI training sets to manipulate model behavior. Can cause models to produce biased outputs, bypass safety filters, or leak sensitive information.
GDPR
The European Union's General Data Protection Regulation governing how personal data is collected, stored, and processed. Important when choosing AI tools that handle your data.
Guardrails
Safety mechanisms built into AI systems to prevent harmful, biased, or off-topic outputs. Includes content filters, topic restrictions, output validation, and behavioral boundaries that keep AI responses within acceptable limits.