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Training Machines to Speak Legalese: The Perils and Promise of AI in Law
As digitalization penetrates nearly every facet of our lives, the legal industry faces some monumental challenges. The massive amounts of data that lawyers need to search, navigate and absorb — including but not limited to legal evidence, litigation data, legal source materials, background research documents, practice area guidance and more — add complexity to even the most mundane tasks.
The sheer volume of information is unprecedented, and the size of the task is “machine scale,” which is to say that it is well beyond human capabilities. To keep pace, we need increasingly powerful and sophisticated algorithms to mine the data, organize it and identify meaningful patterns. These are big challenges, but the payoff can be significant. When AI is deployed appropriately with proper oversight, it helps us make connections we couldn't see before, leading us to new legal and business insights, and providing quick and accurate answers to the questions we have as we try to solve legal problems. But teaching machines how to interpret “legalese” is nearly as challenging as the task it is trying to solve.
The Promise and Pain of Teaching AI
AI needs help from human experts. AI technologies are not entirely autonomous. They need a representative body of data from which to learn — the more the better. But first that data needs to be normalized, structured and accurately labeled or tagged. AI works by finding patterns in data, but “dirty,” inconsistent data can get in the way, and it can teach your system to “learn” the wrong things. Normalizing data typically requires a lot of data analysis before machine learning and other techniques can be successful. In fact, effective AI still relies on regular intervention by human data scientists and legal domain experts who iteratively “teach” machines to make better decisions.
Teaching AI legalese is hard. There is a lot of nuance in legal language, much of which comes in the form of rhetorical persuasion and argument. It can also be hard for machines to distinguish between straight-up “facts” and “legal facts.” Many legal terms are in Latin, and there are countless domain-specific terms and plenty of jargon and abbreviations. Language and terminology varies from one practice area to another. Finally, discerning the specific context in which specific words or passages appear is a huge challenge for AI. With the help of new tools like BERT, we are making big strides in this area, but there is plenty of room for us to get better.
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