AI for Lawyers: Technological Understanding for Compliance and Litigation

There are no formal requirements, but applicants typically have:  

• Relevant professional experience in a connected field 
• A bachelor's degree or equivalent relevant experience  
• Proficiency in English, with the ability to communicate effectively in professional and academic contexts

• Standard rate: £2,950 | Early bird rate: £2,655
• UCL Laws alumni rate: £2,500 | Early bird rate: £2,250
• Student/ Public sector/ charity/ not-for-profit organisations: £1,950 | Early bird rate: £1,755  
• Early bird rates apply if you sign up by 30 November 2025

A 20% discount will be applied to commercial organisations enrolling three or more delegates.

This course is aimed at practising lawyers, legally-focussed policy professionals, or those with an interest in moving to practising in the areas of law and technology.

• Become the bridge in your organisation between technologists and practice.
• Gain the foundations of knowledge you need to keep truly up-to-date with AI for years to come.
• Learn to reliable cut through the hype and understand what is really going on in difficult technological cases.
• Build practical advocacy skills in technology-rich areas through knowing how to appraise disclosures and witnesses.
• Gain rare skills not typically taught in law degrees.

• Upskill your organisation with technical knowledge.
• Save money on external expertise which may not be appropriate or integrated enough with your context and challenges.
• Gain advocates inside your organisation that can detect hype and act with rigour around emerging technologies.
• Better support clients who are looking for legal professionals that can understand their sector.
• Reduce legal risk while supporting innovation with compliance staff that know how to handle emerging technologies, products and services. 

• Day 1: Foundations of AI and Machine Learning
• Day 2: AI in the Real World
• Day 3: When AI Goes Wrong
• Day 4: AI and Emerging Regimes

Delegates will receive four hours of lectures / seminars each day. There will therefore be a total of 16 hours of classroom teaching over the four days. 

There will be no assessment but delegates will receive a certificate of completion provided that they attend at least 12 hours (75%) of classes.  

Michael Veale, Associate Professor in Digital Rights and Regulation, UCL Laws
Michael is a leading academic at the intersection of law and computing. He holds a PhD from UCL Engineering specifically on how law applies to machine learning, and is author of over 50 academic publications in both the top technology law journals and the most selective AI conferences. His research has been used by over 180 different government agencies, courts, regulators, civil society organisations and private companies, is taught at top law and computer science schools around the world, and he is a regular commentator in global media outlets on the trickiest issues of law and technology. Veale sits on the technical advisory board of the Information Commissioner’s Office.

Ravi Naik, Honorary Professor of Practice, UCL Laws; Legal Director, AWO
Ravi is legal director at AWO and honorary professor at UCL Laws. He has been described by POLITICO as one of the ‘28 power players behind Europe’s tech revolution’ and ‘the main legal brain’ behind the largest data rights cases in the UK and Europe, from bringing the original claims against the micro-targeting of Cambridge Analytica (the subject of the documentary The Great Hack); triggering international investigations into the online tracking industry (‘real-time bidding’); and taking some of the most complex cases around machine learning in England and Wales. Naik was the Law Society’s Human Rights Lawyer of the Year 2018-19.

Upon successful completion of this course, you will understand:

• How AI systems are really designed, deployed and maintained in practice.
• What businesses exist in the AI supply chain, and what kind of roles they have in the functioning of these systems.
• What machine learning can do and what it cannot, and what you need to know to tell the difference.
• How to ask the right technical questions of data scientists to get to the legally relevant points, whether in-house or across the courtroom.
• What computer scientists think makes machine learning ‘safe’, ‘fair’ or ‘explainable’, the gaps and challenges in this, and what this really might mean in practical legal contexts.
• What are the biggest, genuine unknowns in both AI and law, and how to navigate and keep abreast of them as they develop.

You will not learn:

• How to code - this alone does not give you the skills needed to be a technology lawyer who can bridge disciplines and mindsets.
• Abstract foundations or arcane details of computer science - this is not feasible in a way that is useful, particularly in a short course, and too disconnected from practice.
• How to use legal technology at work - this course is for those dealing with AI in the law, rather than for the law.