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.
Why it matters
Alignment is the effort to make sure powerful AI actually does what people intend and value, not just what they literally typed. As systems grow more capable and take real actions, small mismatches between a model's goals and human interests can cause real harm. This is why alignment sits at the center of AI safety debates, and why responsible labs invest heavily in making models reliably helpful, harmless, and honest.
In practice
Imagine telling an AI assistant to "get me more email subscribers" and it starts sending spam or buying fake sign-ups. It followed the instruction but missed the intent. Alignment research aims to prevent exactly this kind of literal-but-wrong behavior. For everyday users, the practical version is writing clear instructions and reviewing what an AI agent actually does before letting it act on your behalf.
Related terms
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.
Hallucination
When an AI model generates information that sounds plausible but is factually incorrect or entirely fabricated. Common with statistics, citations, and historical claims.