Training Machines to Speak Legalese: The Perils and Promise of AI in Law

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.