What Are the Practical Use Cases for Generative AI in Legal?
Artificial intelligence (AI) is widely seen as a promising field of technology for the legal sector, with ‘generative’ AI causing a stir in 2022 and continuing to rise in prominence today.
iManage’s Head of Data Science, Jan Van Hoecke, expands below on what generative AI is and what it can accomplish for the legal sector.
Generative AI is making waves, with tools like ChatGPT astonishing users through its uncanny ability to generate reams of sophisticated text with just a few simple prompts by the end user. Exciting as this development is, though, what does it actually mean for the legal space? Is there room for generative AI at law firms and corporate legal departments, and if so, what are the potential use cases?
A Different Kind of AI
Before diving into these use cases, it is helpful to understand a bit about generative AI and the large language models that underpin it.
Unlike machine learning models, which are trained to perform specific tasks and have been employed in the legal space for several years for tasks like document classification, a large language model ingests enough material to create a ‘worldview’ of sorts that it can draw upon to then generate new content based on what it’s already read and absorbed.
How do you ensure, however, that your large language model has an accurate worldview?
One way is via a process called ‘grounding’, which ensures that any answers generative AI produces or any content it creates is grounded in quality content, like the material found in a law firm’s precedents database, document management system (DMS), or other quality sources. This grounding prevents generative AI from going off on wild flights of fancy when it is pressed into service – a prerequisite for any law firm or corporate legal department that wants to take advantage of this technology.
Effortless Unlocking of Knowledge
One of the first areas where generative AI can potentially provide value is around access to knowledge.
For example, what if a knowledge management system (KMS) had a ChatGPT-type interface where users could ask a question and the AI would provide an answer by finding the best candidate documents in the repository or from the external legal knowledge sources? This would be useful if, say, a lawyer is asked to list the different rules for an employee’s right to work in California. Rather than scouring the numerous documents in the system, the lawyer could directly ask the chatbot the question and get the correct answer, complete with details of the reference used to formulate the answer.
Generative AI is making waves, with tools like ChatGPT astonishing users through its uncanny ability to generate reams of sophisticated text with just a few simple prompts by the end user.
While this is precisely the type of task that generative AI is happy to tackle, can its answers be trusted?
Again, we come back to grounding, and the importance of making sure that the large language model is being fed a diet of quality input on which to base its answers and generate content. Grounding ensures that instead of just asking the language model for its opinion, based on its ‘worldview’, it will provide an answer based on the text found in specific documents that the tool has been pointed towards as resources. This capability gives end users more control over the quality of the AI’s results – and thus, more confidence in its outputs.
Less Drudgery Around Drafting
What if generative AI could help not just with searching and finding legal knowledge, but also with helping to draft legal agreements? This is another potential use case for generative AI.
The key, of course, is to make sure that the generative AI leverages what the organisation considers to be the best standards when it comes to writing. This is where firms will want to really lean on the knowledge within the organisation and tap into it. Is there a template for a share purchase agreement for mid-size tech startups that has been endorsed by the subject matter experts in the firm? Consider that as the gold standard that generative AI should be using to ground itself when helping to draft that particular type of share purchase agreement.
Now, does this assist from AI mean that the human is taken out of the equation when it comes to drafting legal documents? No – it just means that more of the grunt work is taken out of the process of writing.
While drafting a legal agreement, the lawyer could ask the AI chatbot to compare a specific clause against the current ‘market standard’, as determined by the organisation’s in-house experts. In some use cases, the legal professional could even go as far as starting off with a set of bullet points for a legal agreement and then have generative AI tap into the proper internal or external resource to help build out the various clauses with the appropriate language. At the end of the day, the human is still formulating the foundational intellectual underpinning of the legal agreement; they are just getting some help in fleshing out the wording.
The key, of course, is to make sure that the generative AI leverages what the organisation considers to be the best standards when it comes to writing.
eDiscovery has always been one of the more AI-driven processes in the legal world, which makes it an intriguing potential area for the ‘new kid on the block’, generative AI.
Traditionally, eDiscovery has relied on supervised machine learning models to do automatic tagging and classification of data while combing through reams of potential evidence. Humans train machine learning models by showing them examples of what type of evidence should fall into what particular bucket, and eventually the machine learning model can tag and classify items with a high degree of accuracy.
Generative AI could help out here in several ways. For starters, imagine a scenario where a file has been unearthed and automatically tagged a certain way during the eDiscovery process, but there is a question as to whether or not it would actually be useful or relevant to the case at hand. Using a ChatGPT-type interface, a legal professional could use perfectly natural human language to ask the AI whether the file meets the criteria or not, and then keep or discard the file accordingly.
Alternatively, generative AI could help streamline the eDiscovery process by automatically generating summaries of contracts, legal briefs, deposition transcripts and other discovered items. This can help busy legal professionals quickly understand the key points and relevant information contained within large volumes of text in a short amount of time.
There is room for generative AI to tackle other, smaller tasks within a legal organisation too, especially in the workflows surrounding matter management. Think how much time is spent on client reporting, or on summarising what was discussed during a conference call or in-person meeting. This is essentially grunt work – but fortunately, it is an excellent candidate for generative AI.
As with the drafting assistance for legal documents, legal professionals can hand generative AI some key points or notes, and AI will expand that into fully written paragraphs. The result? Lawyers can spend more time on the thinking activities that they enjoy rather than uninspiring tasks like reporting or summarising meetings.
For instance, what if users could ask the AI interface in the firm’s DMS a question pertaining to e-billing requirements or data handling issues for a particular client? Rather than the lawyer poring over the Outside Counsel Guideline folder, the AI bot provides the answer, significantly speeding up this internal process.
Beyond improving client-facing aspects, generative AI could even help legal organisations on the back-office side of things, by providing an easier way to interface with the IT help desk and provide answers to IT questions or product queries quicker than ever. These types of front-office and back-office operational efficiencies, taken together, can have a powerful impact on the organisation.
From Scepticism to Success
It is understandable for the legal space to view generative AI with a certain degree of hesitancy and to wonder how – or even if – it fits into the picture. But to write it off as having no practical application for legal professionals would be a mistake.
It is early days yet for generative AI, but use cases are already starting to emerge in the legal space, and principles like grounding are helping ensure that its outputs are on point and trustworthy. Law firms and corporate legal departments would be wise to explore areas where this new technology can potentially be put to use within their own organisations, to help them work smarter, safer, and more efficiently.
Jan Van Hoecke, Head of Data Science
1 Phipp Street, London EC2A 4PS, UK
Tel: +44 02038 796080
Jan Van Hoecke is a highly experienced computer scientist with a passion for technology and problem-solving. His work is concerned with the ever-growing amount of information contained within organisations, and he is resolved to build solutions which help them explore, discover and learn from this knowledge as best they can.
iManage provides an intelligent, secure and cloud-enabled knowledge work platform, enabling organisations to uncover and activate the knowledge that exists inside their business content and communications. Its artificial intelligence and powerful document and email management creates connections across data, systems and people while leveraging the context of organisational content to fuel deep insights, informed business decisions and collaboration.