Convergence Accelerator Phase I (RAISE): Northwestern Open Access to Court Records Initiative

融合加速器第一阶段 (RAISE)：西北大学法庭记录开放获取计划

• 批准号: 1937123
• 负责人: Luis Amaral
• 金额: $ 100万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937123&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Phase; RAISE; Northwestern

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact and potential benefit of this Convergence Accelerator Phase I project is to facilitate easier access to the full range of court records, which should enable more effective systematic research and promote greater analysis on how the federal courts operate and are utilized. The US litigation system is the primary mechanism through which our laws are formally enforced. Understanding how effectively the litigation system operates is critical to maintaining public trust, which is one reason why the courts maintain detailed records of all federal litigation.Development of the proposed Northwestern Open Access to Court Records Initiative (NOACRI) open resource brings together legal scholars, criminologist, sociologists, computer scientists, statisticians, and complexity scholars to build a unique open knowledge network that will enable the convergence of a broad range of diverse communities--including legal scholars, social scientists, economists, journalists, and public policy stakeholders to more systematically study the federal legal system. NOACRI proposes to enable both analytically savvy and inexperienced users to interrogate the court data assembled. The project proposes to create unparalleled access to both raw case data and data annotations, including data annotated by the project team and data that are community-annotated. This more accessible data should also enable development of machine-learning and artificial intelligence (AI) tools to study court data systematically. This project proposes to expand the substantial scholarly potential of court records by bringing recent methodological advances in big data analytics to bear on the field of legal research. To date, researchers have, by necessity, tended to focus primarily on the text of judicial opinions. This project will create an open and free resource of public litigation data, linked to supplementary publicly available data, that will dramatically advance the quantitative understanding of the workings of the federal court system. Importantly, it seeks to make visible and measurable data on cases that are settled or dismissed. Settled and dismissed cases may constitute well over half of federal court activity but is rarely published on court websites or otherwise available for free, limiting systematic study. This research will also aid in the development of data federation standards and natural language querying approaches that will benefit researchers in other subject areas that have a similar need to extract systematic insights from text.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融合加速器支持以团队为基础的多学科努力，以应对国家重要性的挑战，并在不久的将来展示可交付成果的潜力。 这一趋同加速器第一阶段项目的更广泛影响和潜在好处是促进更容易地获得所有法院记录，这将有助于更有效地进行系统研究，并促进对联邦法院如何运作和利用进行更深入的分析。美国的诉讼制度是我们的法律正式执行的主要机制。了解诉讼系统如何有效地运作对维护公众信任至关重要，这也是法院保留所有联邦诉讼详细记录的原因之一。拟议的西北开放获取法院记录倡议（NOACRI）开放资源的开发汇集了法律的学者，犯罪学家，社会学家，计算机科学家，统计学家，和复杂性学者建立一个独特的开放知识网络，使广泛的不同社区的融合-包括法律的学者，社会科学家，经济学家，记者，和公共政策利益相关者更系统地研究联邦法律的制度。NOACRI建议让精通分析和缺乏经验的用户都能询问收集的法院数据。该项目建议创建对原始案例数据和数据注释的无与伦比的访问，包括项目团队注释的数据和社区注释的数据。这些更容易访问的数据还应该能够开发机器学习和人工智能（AI）工具，以系统地研究法院数据。该项目旨在通过将大数据分析的最新方法学进展应用于法律的研究领域，从而扩大法庭记录的巨大学术潜力。迄今为止，研究人员出于必要，往往主要侧重于司法意见的文本。该项目将创建一个公开和免费的公共诉讼数据资源，与补充的公开数据相关联，这将大大促进对联邦法院系统运作的定量理解。重要的是，它力求提供关于已解决或驳回的案件的可见和可衡量的数据。结案和驳回的案件可能占联邦法院活动的一半以上，但很少在法院网站上公布或以其他方式免费提供，限制了系统的研究。这项研究还将有助于数据联邦标准和自然语言查询方法的发展，这将使其他学科领域的研究人员受益，这些领域也有类似的需求，从文本中提取系统的见解。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

The Promise of AI in an Open Justice System

人工智能在开放司法系统中的前景

• DOI: 10.1002/aaai.12039
• 发表时间: 2022
• 期刊: AI Magazine
• 影响因子: 0.9
• 作者: Pah, Adam R;Schwartz, David L;Sanga, Sarath;Alexander, Charlotte S;Hammond, Kristian J;Amaral, Luís A.N.
• 通讯作者: Amaral, Luís A.N.

A user-centered approach to developing an AI system analyzing U.S. federal court data

• DOI: 10.1007/s10506-022-09320-z
• 发表时间: 2022-08
• 期刊: Artificial Intelligence and Law
• 影响因子: 4.1
• 作者: Rachel F. Adler;Andrew R. Paley;A. L. Li Zhao;Harper Pack;Sergio Servantez;Adam R. Pah;K. Hammond;S. O. Consortium

How to build a more open justice system

如何建立更加开放的司法体系

• DOI: 10.1126/science.aba6914
• 发表时间: 2020
• 期刊: Science
• 影响因子: 56.9
• 作者: Pah, Adam R.;Schwartz, David L.;Sanga, Sarath;Clopton, Zachary D.;DiCola, Peter;Mersey, Rachel Davis;Alexander, Charlotte S.;Hammond, Kristian J.;Amaral, Luís A.
• 通讯作者: Amaral, Luís A.