I-Corps: Using Artificial Social Intelligence for Legal Team Assistants

I-Corps项目更广泛的影响/商业潜力是通过将人工智能（AI）纳入发现和分析信息的法律流程，从而实质性地改变法律体系和流程。该项目为法律界提供人工智能工具，使他们能够适应快速变化的信息环境（即，通过在审前取证程序中从大量文件和数据中获取、组织和提取可操作的知识），并利用人机协作的最新进展，有效地向法律团队提供人工智能辅助的取证。目标客户是律师事务所、公司、政府机构和学术研究人员的从业人员，他们对大量非结构化数据进行定性数据分析。这项技术可能有助于法律领域的案件发现过程，因为法律领域通常涉及大量电子存储的信息，以便为诉讼做准备。I-Corps项目基于先进的机器学习算法和社会团队算法，用于提高定性数据分析的效率和向法律团队提供数据结果的有效性。该项目探索了基于人工社会智能的算法的使用，该算法可以自动分析大量非结构化文本数据，从而更有效地洞察数据中的模式，具有更好的个人和编码间可靠性，并且在显著缩短的处理时间内。这种分析的结果将通过基于计算机的代理传递给法律团队，这些代理可以观察周围环境，推断队友的目标，并通过在适当的级别和时间提供信息来协助法律团队。该奖项反映了美国国家科学基金会的法定使命，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 to substantially transform the legal system and processes by incorporating Artificial Intelligence (AI) into the legal process of discovering and analyzing information. This project provides the legal community with AI tools that will enable them to adapt to a fast-changing information landscape (i.e., by procuring, organizing and extracting actionable knowledge from large amounts of documents and data during the pre-trial procedure of discovery), and to leverage the most recent advances on human-machine teaming for an effective delivery of AI-assisted discovery to legal teams. The target customers are practitioners at law firms, corporations, government agencies, and academic researchers and who perform qualitative data analysis on large volumes of unstructured data. This technology potentially aids the case discovery process in the legal field that often involves large amounts of electronically stored information in preparation for litigation.This I-Corps project is based on advance machine learning based algorithms and social teaming algorithms used to improve the efficiency of qualitative data analysis and the effectiveness of data result delivery to legal teams. The project explores the use of artificial social intelligence-based algorithms that automate the analysis of large volumes of unstructured text data to gain insights to the patterns within the data more efficiently, with better individual and inter-coder reliability, and within significantly shorter processing time. The results of this analysis will be delivered to legal teams using computer-based agents that observe their surroundings, infer their teammates goals and assist the legal team by providing information at the appropriate level and time.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.