Jurisdiction(s):
United Kingdom

AI and Machine Learning: A Technical Primer for Lawyers—Data, Training, Algorithms, Neural Networks, Deep Learning, and Key Issues (Explainability, Bias, Data Protection and IP)

Published by a LexisNexis TMT expert

Practice notes

This Practice Note outlines the fundamentals of artificial intelligence (AI) and machine learning (ML) technology. It includes:

A brief history of AI and ML
Why data matters
How ML models are trained
Categories of ML
Factors when choosing or evaluating an ML algorithm
Neural networks
What deep learning means
Typical neural network architectures
Examples of other widely used ML algorithms
Core challenges for AI and ML-transparency, explainability and bias
Privacy and data protection
Safeguarding AI technology

This Practice Note does not address legal or regulatory matters arising from the use or development of AI or ML technologies. For more on these topics, see Practice Notes:

Artificial intelligence-data protection
Artificial intelligence-UK regulation and the National AI Strategy
Artificial intelligence-explainability
Artificial intelligence-intellectual property
Artificial intelligence in the EU-the key legal issues
AI clauses-overview
AI procurement IP clauses-pro-supplier and Contractual considerations for the procurement of artificial intelligence-checklist.

To follow the progress of key legal developments in relation to AI, see Practice Notes: UK artificial intelligence-tracker and EU Artificial intelligence-tracker.

The history of AI and ML

Although frequently perceived (and applied) as...

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Alexander Korenberg , PhD

Kilburn & Strode

Described as “Tech maestro with a solid grip on the legal aspects of patent prosecution and extremely technically savvy” (IAM Patent 1000, 2020), Alexander is known to “build futureproofed patent strategies in cutting-edge areas such as AI” (IAM Patent 1000, 2021). He is listed as one of the world's leading IP Strategists in Intellectual Asset Management's IAM Strategy 300 and have co-edited a book on IP Strategy for the boardroom, based on years of experience advising clients on how to make strategic choices in the wider business context. Alexander leads the firm's AI practice and publishes a monthly newsletter on patenting AI.Alexander has an MSci in Physics from Imperial College and a PhD in Computational Neuroscience from University College London. His particular technical expertise is in Artificial Intelligence (AI) and Machine Learning (ML), having worked at the Gatsby...

Nathalie Richards

Kilburn & Strode

Nathalie is a trainee patent attorney at Kilburn & Strode, where her practice focuses on AI and other emerging technology such as quantum computing. She prosecutes and drafts patent applications and advises clients on their freedom to operate. Nathalie graduated with a Bachelor in Chemistry from the University of Nottingham in 2018 where she particularly enjoyed quantum mechanics and particle simulation using computers. After graduating, she worked as a QA and then as a test engineer for a data company in London, where she implemented an automated testing framework. Prior to joining Kilburn & Strode Nathalie returned to academia to study Financial Computing at Queen Mary University, where she gained a Masters degree with Distinction. Nathalie read modules such as: Machine Learning, Trading and Risk System Development and Advanced Computing in Finance. Her Masters project concerned the Lorenz Lattice Gas Model which can be used to...

Web page updated on 26/05/2026

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