Open-source legal analytics is the art and science of (1) collecting all information relevant to a negotiation or conflict from public sources, (2) iterating over that information to create relationship trees of hidden links and connections and (3) combining all relationship trees and connected data to generate predictive outcomes.
Nowadays, virtually no company has the luxury of drawn-out litigation to resolve a disputed matter. Rather, negotiations and conflicts must often be resolved in a compressed time window, using the best information available. The only way to do this is to take full advantage of open-source legal analytics.
Every negotiation or conflict outcome is, beneath the surface, dictated by values and motives that are intensely human–not by the rote application of legal principles in a vacuum. Further, human behavior is always based on a complex of factors, not a single factor.
And the root of each decision or action is an individual person: a decision-maker or decision influencer with a unique set of values and motives that inform or influence a decision or action. Case rulings are handed down by judges (people) not by courts (abstract institutions). Corporate policies are designed and implemented by corporate officers (people) not by corporations (abstract entities). Appreciating the implications of this truism is the beginning point of legal analytics.
Our work in legal analytics and opponent profiling leverages the most advanced natural language processing programs available for classification, information and relationship extraction, vector and similarity analysis and deep learning, including spaCy and TensorFlow.