Glossary
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.
AI in Legal Practice
from AI & Law
Law firms are deploying AI tools across their operations. Contract review platforms use natural language processing to extract and compare clauses across thousands of documents in a fraction of the time it would take a human team. Legal research tools powered by large language models can summarise case law, identify relevant precedents, and draft first-pass memoranda. Due diligence is being partly automated, with AI flagging change-of-control provisions, unusual liability clauses, and missing documents in data rooms. The profession is moving from scepticism to strategic adoption, but the critical question remains: how do you supervise AI output effectively, and where does professional liability sit when the machine gets it wrong?
Recent Trends
from AI & Law
Generative AI — systems like large language models that create text, code, images, and audio — has moved from novelty to enterprise deployment in under three years. Law firms are building bespoke tools on top of foundation models, and the question has shifted from "should we use AI?" to "how do we govern it?" The proliferation of AI governance frameworks — internal policies covering procurement, use, data handling, and human oversight of AI tools — is creating a new area of advisory work. Deepfakes and AI-generated misinformation pose challenges for evidence law, defamation, and electoral regulation. Meanwhile, competition regulators globally are scrutinising the market structure of foundation model development, where a small number of companies control the compute, data, and distribution layers.
EU AI Act
The European Union's comprehensive regulation classifying AI systems by risk level and imposing corresponding obligations on developers and deployers.
Algorithmic Bias
Systematic errors in AI decision-making that produce unfair outcomes for particular groups, often reflecting biases present in training data.
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.