Artificial Intelligence for Lawyers Explained
January 28, 2021
Incorporating artificial intelligence technology into legal practice is no longer a buzzy new frontier. It’s become accessible in our day to day – and, more importantly, clients have come to expect it from the firms they employ.
When properly deployed and understood, AI can help lawyers make informed, data-driven decisions and improve their efficiency. Here, we help you understand how it works by taking a closer look at the jargon and varying definitions of AI that have permeated the legal industry.
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How is machine learning different from artificial intelligence?
In its simplest form, AI is the overarching description for technologies that use computers and software to create intelligent, humanlike behavior. If you have ever used Siri or Alexa, or conducted a Google search, you have used AI. If you have ever received recommendations for products or services based on past purchasing or browsing history, you have used AI. The list goes on and on. The value in AI is the ability to analyze massive amounts of data and unearth details that are undetectable to the human eye.
Yet without human expertise ensuring the quality and accuracy of that data, AI can do more harm than good. For lawyers, the most successful uses of AI involve both technology and human expertise. That is where machine learning – specifically, supervised machine learning – comes in.
Supervised machine learning is a subset of AI in which computers seek and recognize patterns within predefined data sets. These data sets are typically created by human domain experts who act as guidance counselors of sorts to the machines.
Unlike supervised machine learning, unsupervised machine learning creates data sets without known outputs or predefined data. For these applications, there is no expert-created data set acting as guidance for the tool’s behavior. The software, in essence, learns and adapts to the data on its own.
While in many cases unsupervised learning can uncover new and interesting insights, the lack of guidance and structure can also result in inaccurate findings. For a field that places utmost importance on accuracy, lawyers should be cautious when relying on tools that employ purely unsupervised learning techniques.
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What is machine learning’s role in legal research?
For lawyers, supervised machine learning offers the best of both worlds: faster research than ever, with less risk of inaccuracies or missing documents. The result is nearly instant access to data and insights that can give lawyers a leg up on their competition.
By using tools that employ advanced AI techniques, such as supervised machine learning, attorneys can gather insights from large sets of data and focus on the information that matters most, enabling them to be more efficient, more strategic, and offer more value to their clients.
As an example, Bloomberg Law’s Brief Analyzer employs machine learning to streamline and reduce the steps in traditional brief analysis. The Brief Analyzer tool identifies and evaluates legal authorities cited in the brief, suggests relevant content with detailed explanations for the suggestions, and cross-references and links to related resources such as similar briefs and Practical Guidance.
Bloomberg Law’s Draft Analyzer also uses machine learning to help lawyers compare provisions in their drafts to similarly drafted paragraphs filed in EDGAR. The new tool algorithmically analyzes each paragraph from virtually every agreement and organizational document filed as an EDGAR exhibit to show developing consensus among drafters. It first categorizes each paragraph based on textual similarity and constructs one or more unified versions (“composites”) for each identified cluster of similarly worded paragraphs.
Finally, Bloomberg Law’s Points of Law feature demonstrates how supervised machine learning can enhance a lawyer’s efficiency. Guided by learnings from human-created data sets, Points of Law extracts all of the important and relevant legal principles contained in court opinions. This helps legal researchers unearth new relevant documents and more easily identify similarities between court opinions.
Covering millions of court opinions, this application of AI can minimize the number of errors in the research process and help an attorney avoid missing important, relevant documents. Now the extra time previously spent on research, or outsourcing research, can be better applied to reviewing and acting upon relevant documents and engaging in more strategic work.
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What is natural language processing?
Natural language processing (NLP) is another form of artificial intelligence that enables computers to analyze, understand, and derive meaning from human language. Stated another way, it is the ability of a computer program to recognize and interpret human linguistic patterns and tendencies so that users can submit queries using their natural language instead of “computerspeak.” In the legal industry, this means large amounts of research and data can be queried using common language spoken in the legal world.
For an NLP-based solution to be effective it must be tailored to the specific application. Given that the words, phrases, and entities used in the legal industry have very specific meanings, an NLP-based solution developed for attorneys must account for those meanings and legal concepts.
As an example, Bloomberg Law’s NLP-based search system was developed, in part, by measuring performance against relevance judgments obtained from a panel of legal analysts, as well as by viewing the actual interaction of users with the search system. This evaluation helped the Bloomberg Law search team identify where the system did and did not perform well. Using that information, the team designed system improvements to better understand what attorneys found relevant and address the underperforming queries.
Bloomberg Law’s NLP system also can parse search queries to identify the entities contained within it and assess the relationships between those entities. An “entity” for this purpose could be a statute or regulation, a person, a court, or even a legal concept (e.g., strict liability). Documents discussing those entities are then identified and scored based on how prominently the entities are featured and how they are related in the document.
Natural language processing for legal intelligence
In today’s legal world, this worry of not finding the right information is exacerbated by the insurmountable amount of online legal information – court opinions, agency materials, statutes, regulations, books, practice guides, law reviews, legal white papers, news – the list goes on and on. Information overload has resulted in a lack of confidence in the legal research process, often leaving attorneys unsure of whether they have found the right information, which makes the application of artificial intelligence all the more important.
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As one can imagine, given the amount of legal data, it can also be challenging to accurately identify and disambiguate names of persons and organizations appearing in legal documents. Being able to do this well is important for finding the right information.
For example, in many instances the name of a law firm as mentioned in a legal document may not match the official name of the firm. The name appearing may be an informal version, a misspelling, or even an outdated name. Skadden Arps Slate Meagher & Flom LLP may be shortened to “Skadden Arps Slate Meagher & Flom” or just “Skadden Arps.”
While no single method can perfectly disambiguate names all the time, NLP techniques such as named entity recognition and named entity disambiguation – two active areas of research and practice in the NLP community – can help identify the correct names and return the associated documents an attorney is looking for.
In today’s data-rich world, litigators, transactional lawyers, and corporate counsel alike need to understand which tools and what technologies will best enable them to sift through vast amounts of data to find the one or two critical documents they need.
Although many attorneys will never feel confident in their research, they may sleep easier knowing that their research tools are employing technologies such as NLP and machine learning to help them find that needle in the haystack they are looking for.
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