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

这个I-Corps项目更广泛的影响/商业潜力是开发一个人工智能驱动的过程，用于计算预测民事法律的事项的结果。 目前，律师评估一个新的民事法律的事项的可行性和价值。拟议的技术利用人工智能来标准化，系统化和外部化这个时间密集型和次优的过程。该平台接收新的案件信息，并生成案件评估及其可能的和解价值预测。这代表了通过改善案件选择、提高效率和更好的决策来管理法律的事务的进步。这个I-Corps项目基于人工智能系统的开发，该系统提供民事法律的案件价值和根据分析采取的适当行动。所提出的技术使用建模过程，其中特征和相关权重在多个计算模型中组合，包括数据驱动的人工智能（AI）模型、基于规则的模型和受元分析研究约束的模型。此外，拟议的技术将包括一个使用自然语言处理的集中式数据库，以将收集的案例转化为计算上有用的形式。一个挑战是数据通常是保密的，并且存储在不同的位置。联合学习是一种在多个分散的数据库中训练算法的技术，这些数据库保存本地数据样本，而无需交换数据样本。该方法是联邦学习在法律的数据中的首次应用，能够将产品扩展到众多用户，同时确保隐私和保密性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.