Artificial intelligence in the legal industry is a lucrative field for research and new products. One example of a routine decision making task that is well suited for AI is contract review. The penalties for failing to comply with legal regulations can be very expensive, with fines amounting to several millions of dollars and indirect costs resulting from loss in trust and lost future sales. For a large company with tens of thousands of contracts each year, making sure that each contract complies with local regulations is extremely difficult using only legal professionals.
Rahul Dodhia offers an overview of an AI assistant that can perform routine tasks such as contract review and checking compliance with regulations at higher accuracy rates than legal professionals. The assistant, which can be used by nontechnical professionals, is built around AI augmented by traditional data science methods and a lightweight engineering process. It consists of a family of neural networks that can read a structured document and understand the topic and content of blocks of text. The neural networks can decide if a clause is in compliance or potentially risky. Missing clauses can be added by the assistant from a library of prepared clauses, and risky clauses can be highlighted for an attorney to review.
Rahul outlines the text embedding methodology used, the family of neural networks that read and classify clauses, and the data preparation methods that enabled recall and precision rates well above that of human reviewers. He then discusses an implementation using Keras and CNTK in Python 3.6.
Rahul Dodhia is the director of data science in the Office of the President at Microsoft, currently leading a team of machine learning scientists and engineers in the Corporate, External, and Legal Affairs (CELA) Division. In collaboration with world-class researchers at Microsoft and its partners, his team is developing artificial intelligence applications for the legal industry. His experience in data and analytics spans 20 years, with emphases on statistics, machine learning, engineering for data science, and business applications. Previously, he worked at NASA’s Ames Research Center, Amazon, and Expedia and consulted for several startups. Rahul holds a PhD in mathematical psychology from Columbia University.
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