Generative AI is a type of artificial intelligence that is capable of creating new content based on patterns and data it has learned from existing content.

So, how does it work? Generative AI builds upon extractive AI technology – that is, AI that locates relevant information – and uses machine learning algorithms to analyze patterns in large datasets, such as text or images. The AI then uses these patterns to create new content that is similar in style and structure to the original data but is entirely new and original.

Read more here.

Generally, we use the term Artificial Intelligence (AI) to describe machinebased systems which infer solutions to set tasks and have a degree of autonomy. The scope of these Principles, however, is broader than AI and includes any machine-driven insights resulting from the tools and techniques within the field of data science. These Principles provide high level guidance for anyone at RELX working on designing, developing, and deploying machine-driven insights.

They provide a risk-based framework drawing on best practice from within our company and other organisations. Individual business areas own the practical implementation of the Principles. RELX and its businesses already have robust policies and processes in place that are applicable to AI-enabled solutions. The purpose of the Responsible AI Principles is to complement these. AI is a field that evolves continually, at unprecedented speed and scale.

Read our Responsible AI Principles.

Experienced legal professionals know how case law can change in real time. Depending on the task, this may mean analysing hundreds of documents and data points in response to a brief.
Most notably, AI presents a streamlined opportunity to deconstruct legal documents and uncover insightful recommendations in a matter of seconds or minutes, rather than the hours, weeks, or even months required to review documents manually.

AI can enable a discerning researcher to spot possible risks or opportunities at-a-glance, without necessarily relying solely on prior knowledge, experience, or anecdotal feedback.

Intelligent legal solutions that combine trusted and comprehensive content with sophisticated AI/ML techniques can deliver relevant content to you in context—and should be able to give you deep research results in one click. The AI solution shouldn’t replace the need for your deep expertise; instead, it reduces the time spent on repetitive tasks and frees up your time to focus on applying that expertise to strengthen your position.

Read a whitepaper on Artificial Intelligence in Legal Research and Practice.

1. A competitive edge

In a saturated legal services market that depends on deep insights, speed and foresight, maintaining a competitive advantage as a legal professional is essential. The fast-paced nature of the law means that legal professionals are expected to have a holistic, robust, and real-time overview of every case they’re handling, which they can draw on in court or other proceedings. AI handles the heavy lifting by surfacing information relevant to the matter, leaving you with the comparatively easy task of selecting the most relevant results.

2. Adaptive learning capability

When properly deployed, AI can be a valuable research resource contained within a single software platform. Machine learning algorithms in a research context come pre-trained on massive datasets, meaning they’re already familiar with thousands of cases, precedents, and pieces of legislation. But beyond that, the algorithm may be designed to continue to adapt as it works, learning which results were the most relevant and delivering increasingly similar results over time.

3. Research en masse

After setting up the system and training it to return the most relevant results, legal professionals can use AI to review hundreds of documents, or thousands of datasets at once, identifying information  that relates directly to the researcher’s point of interest. Depending on the task, this can mean reviewing thousands or even millions of documents in a matter of days.

Read a whitepaper on Artificial Intelligence in Legal Research and Practice.

Many articles in legal publications have asked the question, 'is artificial intelligence replacing lawyers?' The answer is no. Rather, AI should be considered as the newest, most junior employee of the business, one that is exemplary at taking direction and churning through significant volumes of content to reach the desired outcome.

Read a whitepaper on Artificial Intelligence in Legal Research and Practice.

ChatGPT could cause problems for workers in three contexts. First, there are situations where the mere use of ChatGPT could violate an employer’s policy. Second, the use of ChatGPT is permissible by the employer, but it’s used in a particular way that could lead to a violation of a law or rule. Third, the worker relies on incorrect information from ChatGPT.

Read more on Implications of Using ChatGPT in the Workplace.

Process transformation comes at a cost, no matter what aspect of the business it applies to. As with all other business transformations, the costs associated with legal AI implementation need to be considered on a lifetime basis. Upfront expenditure is offset and negated by the optimised research efficiency and increased output that comes as a result of the transition.

Read more here.

One of those teething problems can give considerable pause for thought: AI has an unfortunate habit of making stuff up.

Experiences like that underscore the need for reliable tools customized to meet lawyers’ very specific requirements. Open-source models that draw widely on the mass of information available to all worldwide can be wrong – and worse still, they can be wrong with remarkable aplomb and confidence. For Jamie Buckley, however, that can be mitigated with the right kind of generative AI model.

Buckley is chief product officer at LexisNexis Legal & Professional and has been overseeing the incorporation of AI and machine learning into products like Lexis+ for years. “We’re not building a general model that has to answer every question that users can imagine,” he says.
Given that a targeted large language model needs to be trained on the widest possible universe of strictly relevant information, LexisNexis’s 144bn legal and news documents and records that aren’t publicly accessible on the internet are a huge help. That extensive knowledge base means lawyers can easily delve into the latest legal decisions across multiple jurisdictions, for example, even without the resources of larger firms. In Buckley’s view, these technologies can eliminate much of the “drudge work” associated with legal research, document drafting, and other central tasks, and allow lawyers to focus on adding what only they can: judgment, context and understanding.

Read more on Generative AI and the Law.

Copyright concerns around AI training data and information databases remain controversial and there have been instances of companies accused by others of stealing their proprietary data.

While open-source models like ChatGPT draw on whatever users submit and on publicly-available information, lawyers have a professional duty of confidentiality to their clients, meaning that LexisNexis must ensure that search terms or briefs, for example, are not shared or become identifiable.

Read more on Generative AI and the Law.

Reducing the amount of unbillable time spent on case law research is one of the key reasons for any legal business to invest in AI. But investment is only one piece of the puzzle. The key to success is adoption. So, what are some of the key considerations for adopting AI in your legal practice?

Consideration 1: Build vs buy

Consideration 2: People and processes

Consideration 3: Optimising your solution

Read more details on the considerations here.