Glossary
Systematic errors in AI decision-making that produce unfair outcomes for particular groups, often reflecting biases present in training data.
Large Language Model (LLM)
An AI system trained on vast text datasets to generate, summarise, and analyse human language — the technology behind tools like ChatGPT and legal AI assistants.
EU AI Act
The European Union's comprehensive regulation classifying AI systems by risk level and imposing corresponding obligations on developers and deployers.
Explainability
The degree to which the internal logic of an AI model can be understood and communicated to humans — a key requirement for high-risk AI under many regulatory frameworks.
Training Data
The dataset used to teach an AI model to recognise patterns and generate outputs — its quality and composition directly determine the model's capabilities and biases.
Model Risk
The risk of adverse consequences arising from decisions based on AI or statistical models that are incorrect, misused, or inadequately understood.
AI Governance
The internal policies, processes, and controls an organisation puts in place to manage the development, procurement, and use of AI systems responsibly.
Deepfake
Synthetic media — typically video or audio — generated by AI to convincingly depict events that did not occur, raising concerns in fraud, evidence, and defamation.