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?

The EU AI Act — which entered into force in August 2024 with phased implementation through 2027 — is the world's first comprehensive AI regulation. It classifies AI systems by risk level: unacceptable risk (banned outright, e.g., social scoring), high risk (subject to conformity assessments, human oversight, and transparency obligations), and lower-risk systems (lighter requirements). The UK has taken a deliberately different path, adopting a pro-innovation, principles-based framework that empowers existing sectoral regulators (FCA, Ofcom, CMA, ICO) to apply AI-specific guidance within their domains rather than creating a single AI regulator. For firms operating in both the UK and EU, navigating these divergent approaches is a growing compliance challenge.

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.

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.