AI Glossary

The challenge of ensuring AI systems act in accordance with human intentions and values — making them do what we actually want, not just what we literally ask for.

Systematic unfairness in AI outputs caused by skewed training data, flawed labelling, or model design choices that reflect and amplify existing societal inequities.

The set of concerns and practices around protecting personal and sensitive information when using AI systems, covering training data, user inputs, model outputs, and data retention.

Programmatic constraints placed around AI model inputs and outputs to prevent harmful, off-topic, or policy-violating behavior.

When an AI model generates information that sounds plausible but is factually incorrect, fabricated, or unsupported by its training data or provided context.

The practice of monitoring, tracing, and evaluating AI system behavior in production — including LLM calls, latency, costs, retrieval quality, and output correctness.

Systematic adversarial testing of AI systems to identify vulnerabilities, failure modes, and unintended behaviours before deployment — adapted from cybersecurity to probe AI-specific weaknesses like prompt injection and jailbreaks.