Legal AI involves the application of AI technologies, including natural language processing (NLP) and machine learning (ML), to legal tasks.
Natural Language Processing in Legal AI
NLP is a unique software that can interpret ‘natural language’, which is the everyday text we use. Given that law is largely based on written text, the ability to quickly read legal texts using NLP offers a significant new capability that was not previously available to lawyers and clients.
For instance, NLP could be used to review a contract and identify the main clauses, and whether they deviate from the standard clauses typically found in that type of contract. Alternatively, it could be used to comprehend a user’s legal question and then search legal data to return information that accurately matches the concepts in the user’s question.
While this level of sophistication is not flawless or as nuanced as the work of an experienced lawyer, it can pose a strong challenge to a junior lawyer or paralegal.
Machine Learning in Legal AI
Machine learning is the capacity of software to learn and improve its accuracy. In the context of legal AI and text reading, this would mean the ability to enhance its level of precision, often with some human intervention.
Application of AI in Legal Practice
AI can be employed within a law firm or by in-house lawyers. We should not be overly restrictive about how and where certain systems can be used, even if they are currently used by a specific customer group.
As AI applications are essentially ‘tools’, any lawyer could use the systems when there is a use case. They are not restricted to any one practice. The constraints on AI’s use are often more about the users’ creativity than the technology itself. In other words, NLP can be used in a variety of ways and become a handy tool in numerous legal tasks.
Moreover, as non-lawyers also need to handle legal issues, such as agreeing to or referring to legal contracts, some legal AI ‘tools’ are designed to be used by non-lawyers. This is already a growing segment of the legal AI market, particularly in relation to contract generation and completion.
In summary, legal AI can be useful wherever there are people who need to handle legal documents or address legal queries, especially where those legal needs are expressed through text, which AI experts refer to as ‘unstructured data’.
AI in eDiscovery
Regarding eDiscovery, some vendors in this space are using AI software, but not all. Therefore, it isn’t categorized as an AI group on its own. However, AI-driven eDiscovery is most similar to contract analysis.
Legal AI: Three Main Categories
Legal AI can be broadly categorized into three main areas, although new developments continue to expand these categories. The three primary uses of legal AI are:
1. Reviewing Contracts
This involves reading and analyzing legal documents such as commercial contracts and leases, extracting valuable data from them, and comparing them against current laws or rules. In some cases, it also involves assisting individuals in finalizing contracts.
Contract Analysis
Contract analysis involves using NLP to read legal documents such as leases or due diligence documents. The specific tasks that a user or a vendor’s system is designed to perform can vary, but the basic process remains the same. Some of the applications that have been identified by law firms and/or vendors include:
- Due diligence
- Lease review
- Compliance and risk review
- Sales/procurement contract review
- Employment contract review
- Financing/OTC derivative agreement review
- Some types of eDiscovery
NLP is significantly faster than human lawyers at reading contracts, and its accuracy levels in tasks such as due diligence generally surpass those of human lawyers.
Contract Analysis Market Structure
The AI contract review market can be divided into two main product types:
Volume Contract Review:
These systems focus on analyzing large numbers of documents. The goal is usually to identify specific legal issues in contracts and leases. Sometimes the aim is to provide the client with an overall picture of the legal status derived from the group of documents; in other cases, the aim is to identify anomalies (such as in due diligence), or to highlight areas that require further legal attention (such as in a compliance review).
Contract Assistance:
These systems tend to focus on smaller numbers of contracts, sometimes even single contracts. Some vendors design these systems for non-lawyers who want to understand what a contract contains. Some of the systems also focus on the pre-signing phase and help the client identify clauses that the other party has included in a contract that they may need to re-examine, or where they may need to add certain legal clauses to meet standard internal practices/rules for that type of contract.
eDiscovery and AI
While some of the latest litigation eDiscovery platforms use NLP and machine learning to analyze documents, it’s better to view these uses of AI techniques as operating with a parallel, but often quite different use case to contract review for due diligence or lease review, for example. eDiscovery is already a vast legal technology industry in its own right, with over 200 vendors providing a wide range of technologies and methodologies.
2. Legal Data Research
AI systems can also be used in a broader support role beyond contract review. These uses can be roughly divided into:
- Knowledge Systems: These involve legal research along practice lines.
- Predictive Systems: These involve case outcome prediction based on specific matters and/or litigation trends based on court outcomes.
Knowledge Systems
An AI-driven knowledge system is a software that accesses data held or linked to a law firm or in-house team. The data could include expert opinions on legal matters by the partners of the firm, statements of fact about laws and regulation, relevant cases and commentary by judges, as well as any associated case notes or updates that the firm has created itself or is linked to.
Predictive Systems
Predictive systems are a variation on the above knowledge systems and could arguably be called a sub-set of them, though they could also operate on a standalone basis. They are seen as primarily of use in pre-litigation planning. AI-driven software can examine huge numbers of cases and all the publicly available court documents and rulings made by judges in the past up to the present day that are relevant to a case along, with many other types of useful public data.
3. Intelligent Interfaces in Legal AI
The third primary area of legal AI is the creation of intelligent interfaces that can guide lawyers or clients to specific legal information or help them manage their legal needs. The goal of this technology is not so much to conduct primary research or analysis, but to guide a user to the correct outcome.
Data Interfaces
AI-enabled systems can assist clients and lawyers in performing quick and routine legal tasks that require some expert information. In some cases, they may use NLP to understand queries a lawyer or client has typed into a dialogue box. Machine learning may also be used to help the system provide the right answer tailored to the user’s needs.
However, some expert systems do not use AI technology, but rather conditional logic and/or word tagging to understand queries and respond to them. There is a grey area here that vendors are still exploring. But even those not using AI systems seem likely to move in that direction eventually.
These applications are often used when a person is guided through an ‘intelligent checklist’ that allows them to gain the right knowledge, or in some cases to complete very simple legal documents, such as NDAs.
The software usually uses drop-down menus and checkboxes to guide the user through a series of steps so that they can either be given the correct data they need, for example in response to a specific legal question, or be used to fill in the missing elements of a standard document.
They may be outward-facing for clients to use, or inward-facing, allowing lawyers to interrogate the expert system for their own specific needs and/or help them to complete a legal document.
Expert systems, whether outward-facing or inward-facing, are carefully designed to provide informational support to a specific need, such as how to respond to a certain type of employment dispute, or to help add data to a certain type of legal form.
Triage Services
There is potential to use an outward-facing AI system to act as a triage service. Currently, most law firms do not use these types of systems, though banks and other financial service companies are developing automated customer relations systems.
Such triage/customer-directing interfaces already exist in a very basic way at some law firms – usually those that deal with the public and ask clients to write a short email to describe the matter. However, these could be far more effective and not just serve individual clients.
Rather than asking the client to do all the work, an AI system could be used to help guide clients to the right outcome in terms of an advisory path and understand their queries using NLP. It could also make use of machine learning to steadily improve its responses to certain types of client query over time. The AI could also immediately link the information provided via the triage system to the firm’s own research into the types of case worth pursuing, as well as link to the firm’s CRM system.
Legal Bots
Many lawyers will be aware of ‘bots’ or ‘chat bots’, though perhaps without considering how they could be used in the legal space. Apple’s Siri is probably the best-known ‘bot’ – what one might call an AI-driven personal assistant.
Currently, there are ‘access to justice’ legal bots that operate using written text, which help to give preliminary advice on matters such as criminal law to members of the public. Another example is a bot that guides members of the public through the process of completing a challenge to a parking fine.
However, these systems are, as yet, relatively narrow. That said, the market for legal bots is continually evolving and there are already new bots surfacing that are capable of a far broader range of legal topics.
Wrapping Up Legal AI
This is a concise overview of legal AI, a field that is rapidly progressing. Almost every week, a new legal tech start-up introduces an application that utilizes NLP and machine learning techniques, making the landscape more complex than this simplified version. However, everyone has to start somewhere, and familiarizing oneself with the key aspects of legal AI is a good starting point for structuring one’s understanding.
The Evolving Landscape of Legal AI
By the end of 2024, the legal AI market will likely have transformed significantly. More AI companies will surface, potentially integrating several of the aspects mentioned above. Some may merge, or devise entirely new methods of applying NLP and machine learning in the legal sector. It’s a dynamic field, making it all the more important to keep up-to-date.
The Future of Legal AI
One thing is clear: we have transitioned from a period of speculation to a time of practical and real-world applications of legal AI systems. The number of vendors will rise, and the number of law firms, in-house teams, and non-lawyers using these AI systems will increase. Eventually, legal AI will become a crucial component of the legal sector that many thousands of people depend on and use daily, much like many other technologies before it.
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