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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.
The DOJ's New Parameters for Evaluating Corporate Compliance Programs
The parameters set forth in the DOJ's memorandum have implications not only for the government's evaluation of compliance programs in the context of criminal charging decisions, but also for how defense counsel structure their conference-room advocacy seeking declinations or lesser sanctions in both criminal and civil investigations.
The DOJ's Corporate Enforcement Policy: One Year Later
The DOJ's Criminal Division issued three declinations since the issuance of the revised CEP a year ago. Review of these cases gives insight into DOJ's implementation of the new policy in practice.
Use of Deferred Prosecution Agreements In White Collar Investigations
This article discusses the practical and policy reasons for the use of DPAs and NPAs in white-collar criminal investigations, and considers the NDAA's new reporting provision and its relationship with other efforts to enhance transparency in DOJ decision-making.
Bankruptcy Sales: Finding a Diamond In the Rough
There is no efficient market for the sale of bankruptcy assets. Inefficient markets yield a transactional drag, potentially dampening the ability of debtors and trustees to maximize value for creditors. This article identifies ways in which investors may more easily discover bankruptcy asset sales.
A Lawyer's System for Active Reading
Active reading comprises many daily tasks lawyers engage in, including highlighting, annotating, note taking, comparing and searching texts. It demands more than flipping or turning pages.