AI Case Study

"The LawGeex AI has been trained to detect issues on more than a dozen different legal contracts, ranging from software agreements to services agreements to purchase orders. This specific research focused solely on NDAs – the most common form of business
contract. NDAs are typically used to create a legal obligation to secrecy, and compel those who agree to them to keep information confidential and secure. The LawGeex AI was trained on tens of thousands of NDAs, using custom-built machine learning and deep learning technology. The machine was trained based on an exclusive corpus of documents that presented the LawGeex algorithm with a variety of examples, which allowed it to distinguish between different legal concepts. This level of technology for analyzing legal documents has only been possible with advances in computing over the last five years. Computers convert the text into a numeric representation. The image below is a visualization of how computers read text. Each dot represents one paragraph in the semantic space. The different colors shown represent different legal issues. Pink dots, for example, represent samples of non-compete issues, and purple ones represent governing law sections.

LawGeex created proprietary Legal Language Processing (LLP) and Legal Language Understanding (LLU) models for the task. Teams of lawyers and engineers taught LawGeex AI legalese by exposing the AI to a wide range of legal documents. Once the AI learned legalese, legal trainers pointed out the concepts it is required to recognize. The LLP technology allows the algorithm to identify these concepts even if they were worded in ways never seen before. Monitoring concepts, not keywords — LawGeex AI operates in a far more sophisticated manner than a blunt “keyword search.” Keyword searches can be over- and under-inclusive, as words may be absent from relevant documents, or present in irrelevant documents. True AI recognizes a concept however it is phrased or wherever it appears in a document. Unsupervised learning was used for teaching the AI engine the core legalese language. Thereafter, supervised learning, using deep learning multi-layer LSTM and convolution technology, was used to train the system for the fine-tuned issue-spotting. Supervision was performed based on human-annotated documents, using legal experts. A unique augmentation algorithm was applied to boost learning from these examples."