I-Corps: Artificial intelligence-driven process for computationally predicting the outcomes of civil legal matters

Award ID: 2042471

The broader impact/commercial potential of this I-Corps project is the development of an artificial intelligence-driven process for computationally predicting the outcomes of civil legal matters. At present, attorneys estimate the viability and value of a new civil legal matter. The proposed technology leverages artificial intelligence to standardize, systematize, and externalize this time-intensive and suboptimal process. The platform intakes new case information and generate both an assessment of the case and a prediction of its likely settlement value. This represents an advance in managing legal affairs by improved case selection, increased efficiency, and better decision-making. This I-Corps project is based on the development of an artificial intelligence system that provides civil legal case values and the appropriate action to take in response to the analysis. The proposed technology uses a modeling process where features and associated weights are combined across multiple computational models, including data-driven artificial intelligence (AI) models, rule-based models, and models bounded by meta-analytic research. In addition, the proposed technology will include a centralized database using natural language processing to render the garnered cases into a computationally useful form. One challenge is that the data are often confidential and stored in disparate locations. Federated learning, a technique that trains an algorithm across multiple decentralized databases holding local data samples without exchanging the data samples, will be deployed. The method, which is the first application of federated learning to legal data, enables expansion of the product to numerous users while ensuring privacy and confidentiality. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ruth Shuman