What is AI in Legal Analysis?
To understand how AI works in the legal analysis arena requires some basic knowledge of what AI is, why it has taken such a prominent seat at the table of technology, and why it is being used so much more frequently.
Most have heard of AI, but probably cannot give a clear concise description of what AI really is. AI takes many forms but we are concerned primarily with its application in the utilization of algorithms. It is an area of computer science that comes under the umbrella of machine learning.
Machine learning focuses on decision making or perception. It is the application of artificial intelligence. Machine learning is used in many industries including: manufacturing, medical, finance, shipping and logistics, technology and yes, the legal arena.
Two technologies that have spurred the growth of artificial intelligence are cloud computing and big data. Cloud computing has had a significant effect on data storage. With cloud computing data is not created in its own silo, with limited use. Rather it is accessible for analysis which allows data to be combined and compared, and ultimately govern decisions.
Big data utilizes analytics which is defined as, "a set of computer-assisted methods and techniques for collecting, organizing, analyzing and presenting large volumes of data. In law, data analytics enables lawyers to capture, organize, analyze and visualize a wide variety of unstructured information . "
One of the challenges of big data is that it relies heavily on human input to identify and suggest relevant correlations, contextual associations, patterns or trends across massive stores of data. AI augments big data by automating aspects of the data analytics process. As a consequence, AI applications consume and process enormous volumes of legal data.
AI with its algorithms can be used to automate and enhance data tagging, association and relevant analysis for processing legal documents. Legal documents can be classified and categorized to increase efficiency and accuracy. Classification systems can even allow you as the user to dictate what information is required and how simply or complex the requirements shall be.
In the area of legal research, legal document analysis has shown dramatic improvements in cost efficiencies with the applications of AI. Previously legal reading required humans to slowly read and absorb legal text as well as apply their legal training, education and experience to extract the details. Only humans could make analogies to other legal documents or facts and determine whether or not there was a distinction that was meaningful or not.
However, the ability of AI to read, digest and categorize legal text has resulted in impressive levels of accuracy in classifying and determining the relevance of legal documents.
Ways in which AI Benefits Legal Document Analysis
The most prominent benefits of using AI for legal document analysis are time savings, cost efficiency and increased accuracy. Finding a relevant case or statute can take lawyers hours or days, even with legal research assistants or interns. AI can analyze information much more quickly, consistently and accurately than a person. Increases in productivity can be even greater if you consider that in typical legal practice, many of the same documents (e.g., Non-Disclosure Agreements, Employment Agreements, Client Engagement Letters) are generated over and over again; but, there are many other types of documents (e.g., Bond Indentures, Development Agreements, Partnership Agreements, Security Agreements) that are often times drafted for just one deal, one client, one transaction one of a kind event. As any experienced attorney knows, the next time we see such a document, it is almost always different. Despite the minor (and major) differences from deal to deal, an experienced attorney knows that there are specific provisions that must be included for our clients protection. One of the ways AI can save time and reduce costs (and reduce risk to clients) is by powering a lawyer’s ability to find boiler plate clauses and non-boiler plate clauses that require a lawyer’s judgment or discretion for review and editing in a document from a document template. AI can quickly identify relevant documents and can also help identify them based upon the applicable jurisdiction and case law (jurisdictional analysis is especially useful for international transactions). AI will also provide support for attorneys with suggested edits that are supported by case law.
Common Uses of AI in Legal Document Review
In the evolving landscape of legal practice, AI has found significant applications that enhance efficiency and reduce costs in legal work. Three major areas where AI is making a substantial impact are contract review, due diligence for mergers and acquisitions (M&A), and legal research.
Contract review is a crucial part of any commercial transaction, and with the complexity involved and the volume of documents typically generated, this process is often time-consuming and costly. AI offers powerful tools that can review, extract, and correct contract terms and clauses at an unprecedented speed and accuracy. For example, AI can be utilized through automated contract management systems that not only track and store contracts but also review them for compliance with legal standards and flag provisions that may pose risks. These systems can compare existing contracts across jurisdictions, identify inconsistencies, and generate summaries of key contractual terms. One popular example is the use of AI-powered contract review tools that allow lawyers to import contracts and have them analyzed and marked up with suggested changes almost instantaneously.
Due diligence is another area that has seen benefits from AI. Particularly in the context of M&A, there is substantial value in being able to quickly and accurately digest dozens, if not hundreds, of contracts in order to identify red flags. Traditionally, this would involve a large team including lawyers, paralegals, and other professionals going through contracts line by line. This process is labor-intensive, expensive, and most importantly, prone to human error. AI tools can sort through massive amounts of agreements to spot critical issues, risks, or hidden structures. Notably, one of the main use-cases for AI in M&A is in supporting due diligence processes by quickly identifying unusual terms and suggesting alterations or best practices.
Finally, legal research is an area that has long been time-consuming for lawyers. It’s common practice for lawyers to spend hours weeding through legal codes, regulations, or caselaw. While tools have long existed to assist lawyers, only recently has AI been able to boost legal research to a new level of power and sophistication. For example, machine learning can help narrow down cases based on particular fact patterns that may be relevant to a given client matter, reducing the time spent on searching cases. AI is also being used to predict outcomes from cases by examining a vast array of past cases and their eventual rulings. Some AI tools allow lawyers to upload their own briefs and markups, and based on their input, the AI will suggest optimized briefs or summarize findings from thousands of rulings.
Limitations and Issues
AI is not without its limitations and challenges in the realm of legal document analysis. One of the primary concerns is data privacy. As legal documents often contain sensitive, confidential, or proprietary information, ensuring that data is handled correctly and held secure while still allowing for the powerful analysis of documents is a constant balancing act. Proper safeguards are necessary to ensure that the AI systems are compliant with regulations such as GDPR or HIPAA, and that unauthorized access to sensitive data is strictly controlled. Another limitation is the potential over-dependence on technology. AI and machine learning, while powerful, are not infallible . They might miss nuances in language, incorrectly categorize documents, or provide biased results based on the data sets used to train them. It’s essential to have human oversight, not just to ensure accuracy and fairness, but also to provide an interpretative layer that takes into consideration context and nuance that a machine may miss. Finally, it’s worth noting that AI can never fully replace the skills of a trained lawyer or legal professional. Legal document analysis requires a level of subjectivity and comprehension that, at least for now, only human beings possess. AI may assist in accelerating document review, but it is always important to have an experienced attorney interpret the findings and provide context and additional support when it comes to making legal decisions.
Future Directions of AI Legal Document Analysis
As artificial intelligence continues to evolve, the future trends in AI-based legal document analysis are becoming increasingly important. The legal industry is poised for a significant transformation as new technologies emerge to streamline processes and improve efficiency. Natural language processing (NLP) and machine learning algorithms are at the forefront of these developments, enabling more accurate legal analysis and quicker insights into legislation, regulations, and case law.
One of the most significant future trends in legal AI is the increased use of NLP to simplify the complex legal lexicon. By using NLP algorithms, AI software can decipher the meaning behind legal jargon and present it in a more digestible format. As NLP technology improves, AI tools will be able to analyze legal language more accurately, making it easier to identify relevant information and automate responses to common legal queries.
Another major trend is the continued advancement of machine learning algorithms, which allow AI system to "learn" from data patterns and improve over time. This will enable legal AI software to adapt to the specific needs of different law firms or legal departments, tailoring its analysis and recommendations to the unique style and preferences of its users. Machine learning will also allow AI systems to better understand the nuances of human language, making them even more effective at legal document analysis.
The impact of artificial intelligence on legal job roles is another area of focus for the future. As AI systems automate routine tasks like legal research or document review, there is concern that many traditional legal jobs may become obsolete. However, many experts believe that this will not be the case and that lawyers will continue to be needed to provide essential services like client communication and strategic legal advice. Instead of replacing legal jobs, AI may actually create new roles for legal professionals, who will be needed to oversee and manage AI-based systems and ensure they are being used ethically and effectively.
In conclusion, the future of AI-based legal document analysis is bright, with many exciting trends on the horizon. As these technologies develop, it will be important for legal professionals to keep abreast of the latest advancements and best practices to ensure they are getting the most benefit from these powerful new tools.
Selecting the Appropriate AI Tool for Legal Analysis
In considering the right AI tool for legal document evaluation, when evaluating whether a particular tool meets a law firm’s requirements, the following are some key areas to consider:
Ease of use: How user friendly is the interface? If attorneys will be expected to use the tool, in what ways will the solution need to be adapted to be able to be used by this group? Is an internal champion required?
Compatibility with existing systems: How well does the solution integrate with other corporate software programs? For example, does it work with the existing workflow software? Another consideration is the program’s ability to integrate with other AI solutions.
Scalability: Beyond simply being flexible in its use , one of the most important factors in choosing an AI solution is scalability. Scalability here is referring to how well will a particular solution function at all phases of a case, from initial intake to early decision making, up to trial. If, for example, your analysis reveals that some version of a case is worth taking on at pre-litigation stage, how well will the solution assist the firm in leveraging big data to evaluate the case prior to proceeding with discovery?
Also, how well will the solution handle the huge influx of data, e.g., documents, interrogatory responses, and deposition transcripts, that correspond with a full-blown litigation?
Vendor support: It can be expensive to make this type of investment. You will want to feel comfortable that the vendor will be around to assist you with future needs and developments.