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.
Why it matters
AI learns from human-created data, so it absorbs the patterns and prejudices baked into that data. This matters because biased systems can quietly disadvantage people in hiring, lending, and other high-stakes decisions, often without anyone noticing. Even for casual use, it's worth knowing that AI reflects its training data's blind spots, so its "neutral" answers can carry assumptions you'd want to question.
In practice
If a resume-screening tool was trained mostly on past hires from one background, it may learn to favor that profile and unfairly downrank equally qualified candidates who look different on paper. The AI isn't malicious; it's echoing skewed history. That's why bias matters most wherever AI influences real decisions about people, and why a human should review outputs that affect someone's opportunities.
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.
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.
Hallucination
When an AI model generates information that sounds plausible but is factually incorrect or entirely fabricated. Common with statistics, citations, and historical claims.