STOP PRESS: Regulation (EU) 2026/1744 amending Regulation (EU) 2024/1689, Regulation (EU) 2018/1139 and Regulation (EU) 2023/1230 as regards the simplification of the implementation of harmonised rules on artificial intelligence (Digital Omnibus on AI) was published in the Official Journal on 24 July 2026 and entered into force on 27 July 2026. This Practice Note will be updated shortly to reflect amendments to Regulation (EU) 2024/1689, the EU Artificial Intelligence Act. For further information on the changes introduced by the Digital Omnibus on AI, see Practice Note: EU Digital Omnibus—tracker and News Analysis: Digital Omnibus proposal—re-writing the EU's digital rulebook. The integration of Artificial Intelligence (AI) in the workplace is transforming business operations, offering both opportunities and challenges. AI can automate repetitive tasks (eg sorting CVs, drafting job descriptions), allowing employees to focus on higher-value activities, but it also raises concerns about bias and discrimination, particularly in human resources (HR) management

Technology with the ability to perform tasks that would otherwise require human intelligence and which, usually, have the capacity to learn or adapt to new experiences or stimuli, including machine learning, speech and natural language processing, robotics and autonomous systems.

This Practice Note considers the practical applications of artificial intelligence (AI) and automation technologies in the e-commerce sector in the UK and the key legal issues that arise from their use. It looks at how AI and automation are used by businesses in the e-commerce sector both from a business to business (B2B) perspective and also from a business to consumer (B2C) perspective. For an introduction to B2B e-commerce, including the types of platforms and technologies typically utilised, see Practice Notes: • Digital commerce—introduction • Business-to-business digital commerce—compliance and regulation For an introduction to B2C e-commerce including an outline of the key legal issues which businesses need to consider when trading with consumers online, see Practice Note: Business-to-consumer digital commerce—compliance and regulation. For more information on B2C routes to market, see also Practice Note: Digital sales channels in B2C digital commerce—introduction. Background The UK e-commerce landscape has been dramatically changed and continues to be rapidly transformed by the use of AI and automation technologies. These technologies are being applied in areas ranging

FORTHCOMING DEVELOPMENT: On 3 February 2025, the House of Commons Treasury Committee launched an inquiry into AI in financial services to explore how UK financial services, including pensions, can take advantage of opportunities in AI while at the same time mitigating threats to financial stability (eg cybersecurity risks) and safeguarding financial consumers, particularly vulnerable customers who may be at risk of bias. For further information, see LNB News 04/02/2025 12. What is artificial intelligence (AI)? Artificial intelligence (AI) refers to computer software and systems that are capable of demonstrating human intelligence. They can learn, plan, reason or process natural language as they go rather than only relying on pre-programmed tasks. With AI expected to transform financial services, it is in the best interests of pensions professionals, trustees, employers, providers and others involved in the running of pension schemes to consider the opportunities and risks which AI may present (both now and in the future). Any consideration of AI will inevitably require some familiarity with certain technical terms, including: • extractive AI vs generative AI—while

Introduction This Practice Note discusses some of the key issues arising where a supplier uses artificial intelligence (AI) in its provision of services to a customer under a services agreement. This Practice Note is not concerned with the procurement of an AI system itself, but rather with the scenario where a supplier uses AI in the background as part of its service delivery model. It is assumed that the supplier is using some form of generative AI system, such as ChatGPT or Microsoft Copilot, or potentially its own equivalent. The range of circumstances in which this could occur is vast. For example, a supplier might use AI to help analyse information or documents and provide advice as part of a consultancy type service or it might use AI to assist with creating outputs/deliverables such as advertising copy as part of a marketing service. Whatever the use case or particular services might be, the following common themes are likely to arise: • the scope of AI use: how to define this contractually, whether to control which AI systems

Introduction This Checklist is based on Practice Note: Artificial intelligence and services agreements and is designed to help customers when reviewing and negotiating services agreements where artificial intelligence (AI) is used by the supplier in the background as part of its service delivery model in order to fulfil the services. It focuses on the provision of services in the UK. Defining and controlling AI use • Ensure AI use is sensibly scoped and described in the services agreement (eg in the services description/specification) • Ensure AI use remains within the agreed scope • Consider whether to specify the AI systems that can be used and whether consent can be required for new AI systems or for replacing existing AI systems • Assess whether changes to the functionality of the AI systems can be controlled in any way • Where changes to the AI systems are permitted without consent, consider if it is possible to include termination or other rights for material adverse changes • Try to require the supplier to comply with the customer’s AI use

This Practice Note explains the ways in which artificial intelligence (AI) is used in the UK criminal justice system and offers some predictions as to how it may be used in the future. Following roughly the lifecycle of a criminal case, from investigation to sentencing, the various uses of AI are highlighted and considered. This Practice Note adopts the working definition of AI that is used in the government’s March 2023 White Paper, ‘A pro-innovation approach to AI regulation’, being a system that displays two characteristics: • the first is ‘adaptivity’. This means that AI systems can be trained by inferring patterns in data which are often not easily discernible to humans, and make new inferences themselves • the second is ‘autonomy’. This means that AI systems can make decisions without the express intent or ongoing control of a human Technology based on machine learning is likely to fall within the above definition, because it involves systems developing in a dynamic

STOP PRESS: Regulation (EU) 2026/1744 amending Regulation (EU) 2024/1689, Regulation (EU) 2018/1139 and Regulation (EU) 2023/1230 as regards the simplification of the implementation of harmonised rules on artificial intelligence (Digital Omnibus on AI) was published in the Official Journal on 24 July 2026 and entered into force on 27 July 2026. This Practice Note will be updated shortly to reflect amendments to Regulation (EU) 2024/1689, the EU Artificial Intelligence Act. For further information on the changes introduced by the Digital Omnibus on AI, see Practice Note: EU Digital Omnibus—tracker and News Analysis: Digital Omnibus proposal—re-writing the EU's digital rulebook. This resource kit contains a list of the key practical guidance available across Lexis+® UK that deals with artificial intelligence (AI). It is laid out by practice area and updated as new content is added. The rapid explosion in AI technology has meant that legislators, businesses and the public have become increasingly focused on the potential benefits and risks associated with the use of AI. AI raises a variety of legal

Date: [insert date] 1 Introduction Artificial intelligence (AI) has the potential to transform the way we work, improve the services we provide and boost our competitiveness. We must ensure we use AI tools in a secure and responsible way, respecting confidentiality and third party rights. This includes any AI tools used by third parties on our behalf. [state briefly what has triggered this board briefing, eg ‘We are already using AI in our internal operations in a limited way. The purpose of this briefing is to explain what sort of AI tools we are currently using, the technology that is available, the risks associated with AI and the measures we have in place to mitigate or manage those risks.’] 2 What is meant by AI? There is no single definition of AI. Broadly speaking, it is the simulation of human intelligence in machines, generally computer systems. AI tools can learn, problem-solve, make decisions and understand language. This can be contrasted with non-AI pre-programmed tools, which generally apply the same set of rules each time unless a human intervenes to update

This Practice Note considers the interaction between artificial intelligence (AI) and copyright law. Lawmakers across the world are grappling with how to ensure that right holders maintain the high levels of copyright protection afforded to their works under national laws while at the same time not stifling innovation and remaining attractive jurisdictions for AI model developers. Uncertainty remains around questions such as: what permission is required from right holders to train AI models? when does training amount to copyright infringement? to what extent do existing exceptions apply? how does the territory in which training occurs impact the enforceability of a claim? This Practice Note outlines the various approaches taken to address these challenges in the UK and EU through consultations, guidance, legislation and case law, as well as industry initiatives. US policies and decisions are also briefly discussed. It also deals with the rights in the instructions and prompts, as well as the training algorithms themselves, and examines whether and how AI outputs can be protected under copyright laws, labelling requirements for content created by AI, and how

Why use artificial intelligence in design? The growing prevalence and availability of artificial intelligence (AI) technologies, in particular generative AI, presents changes, opportunities and challenges for almost every sector, with design being no exception. AI can serve as a powerful tool for brainstorming and conceptual development, helping designers overcome creative blocks and explore a wider array of visual solutions. AI can also boost efficiency—designers can use generative AI to produce multiple design options quickly. This can accelerate the creative process and enable experimentation, potentially leading to highly innovative outcomes. However, there are several legal risks associated with the use of AI tools in the design process. This Practice Note considers how IP practitioners advising clients in design should respond to the growth of use of AI tools in the industry, by identifying and discussing these risks. It covers ownership of IP rights in computer-generated designs and works without a human author, challenges for protecting designs generated wholly or partially by AI and the risks of infringing third-party IP rights. It considers the role that AI tools can play in

STOP PRESS: Regulation (EU) 2026/1744 amending Regulation (EU) 2024/1689, Regulation (EU) 2018/1139 and Regulation (EU) 2023/1230 as regards the simplification of the implementation of harmonised rules on artificial intelligence (Digital Omnibus on AI) was published in the Official Journal on 24 July 2026 and entered into force on 27 July 2026. This Practice Note will be updated shortly to reflect amendments to Regulation (EU) 2024/1689, the EU Artificial Intelligence Act. For further information on the changes introduced by the Digital Omnibus on AI, see Practice Note: EU Digital Omnibus—tracker and News Analysis: Digital Omnibus proposal—re-writing the EU's digital rulebook. This Practice Note explains the basics of artificial intelligence (AI) and machine learning (ML) technology. It covers: • The history of AI and ML • The importance of data • Training an ML model • Types of ML • Considerations when selecting or assessing an ML algorithm • Neural networks • What is deep learning? • Common neural network architectures