NRT-HDR: Computational Research for Equity in the Legal System" (CRELS)

NRT-HDR：法律体系公平的计算研究”（CRELS）

• 批准号: 2243822
• 负责人: David Harding
• 金额: $ 300万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2243822&HistoricalAwards=false
• 关键词: NRT; HDR; Computational; Research; Equity

The criminal legal system is an important driver of inequities and social and economic polarization, and legal institutions are at the leading edge of use – and misuse – of artificial intelligence. The increasing availability of “big data” from (and about) criminal legal systems – and the people who are enmeshed in them – provides a new opportunity to illuminate inequities and their sources. This National Science Foundation Research Traineeship (NRT) award to the University of California, Berkeley will develop novel interventions to reduce inequities and their resulting harms in criminal legal systems. New scientific knowledge will be generated through the development of tools for large-scale, "human-in-the-loop" analysis of criminal justice data, and will be used in the generation of new insights regarding legal system processes, impacts, and institutions. Faculty and trainees will collaborate across disciplines to simultaneously address social-science and policy questions regarding equity and criminal legal institutions, the development of tools and methods for leveraging newly available data from the criminal legal system, and ethical and social implications of big data and AI in the context of criminal justice. This NRT will train a new generation of researchers interested in computational approaches to equity and legal systems, enabling them to develop and evaluate public policy solutions that can mitigate social and economic polarization. It will also train a diverse workforce with flexible and transferrable computational skills, while also training social and data scientists in ethical AI and its social implications. It will create a transformative, cross-disciplinary model for graduate training at Berkeley and elsewhere, while also developing a broad-based recruiting and mentoring program to enhance training of students from underrepresented groups, which, in turn, helps to diversify the STEM workforce. The project anticipates training 50 PhD students, including 25 funded trainees, from the Social Sciences, Computer Science, and Statistics. Recent public and policy interest in the criminal legal system coupled with new government efforts to make data public and leverage data for public policy creates new opportunities to study the criminal legal system, but only if such data can be made ready for analysis. The criminal legal system is critical terrain for evaluating how pervasive data collection and algorithmic decision-making can be brought into the service of society, while addressing potent challenges that can accompany these approaches. Big data and AI can give us broader and more precise knowledge of the dynamics of social systems and hold potential to increase transparency and support fairer decision-making. At the same time, areas relating to criminal justice have seen a massive expansion of surveillance, data production and reuse, and algorithmic decision-making often without oversight, recourse, or evidence about effectiveness in addressing underlying issues. Data technologies in criminal justice have grounded new social schema of classification and accompanying social hierarchies – from recidivism risk scores to predictive policing – with important implications for opportunity and life chances. Our goal is to develop tools for continuous ingestion, integration, and cleaning of structured and unstructured data, and the analysis of such data. Using a combination of large pre-trained AI models coupled with data management and human-computer interaction techniques, we will develop tools to ingest information from various government and online sources, turning it into structured data for analysis. These efforts will lead to novel and generalizable tools for semi-autonomous and continuous data processing, as well as integration at scale, that also preserves privacy and promotes equity.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.

刑事法律制度是不平等和社会和经济两极分化的重要驱动力，法律机构正处于人工智能的使用和遗物的领先优势。犯罪法律制度（以及被吸引在其中的人们）的“大数据”越来越多的可用性为照亮不平等及其来源提供了新的机会。这项国家科学基金会研究实习生（NRT）授予加利福尼亚大学伯克利分校，将制定新的干预措施，以减少不平等现象及其对犯罪法律制度的危害。通过开发用于刑事司法数据的大规模“人类”分析的工具来生成新的科学知识，并将用于生成有关法律体系程序，影响和机构的新见解。教师和学员将在跨学科中进行合作，以同时解决有关公平和刑事法律机构的社会科学和政策问题，开发工具和方法，用于从刑事法律制度中利用新近可用的数据以及在刑事司法背景下对大数据和AI的道德和社会影响。该NRT将培训对公平和法律制度计算方法感兴趣的新一代研究人员，使他们能够开发和评估可以减轻社会和经济两极分化的公共政策解决方案。它还将以灵活和转移的计算技能培训潜水员的劳动力，同时还要培训社会和数据科学家的道德AI及其社会影响。它将创建一个在伯克利和其他地方的研究生培训的变革性，跨学科的模型，同时还开发了一项基于广泛的招聘和心理计划，以增强来自代表性不足的群体的学生的培训，这反过来又有助于使STEM劳动力多样化。该项目预计将从社会科学，计算机科学和统计数据中培训50名博士生，包括25名资助的学员。最近对犯罪法律制度的公共和政策兴趣，加上政府公开数据并利用公共政策数据的新努力创造了研究犯罪法律体系的新机会，但前提是可以准备好进行分析。犯罪法律体系是评估如何将普遍数据收集和算法决策带入社会服务的关键地形，同时解决可以适应这些方法的潜在挑战。大数据和人工智能可以使我们对社会系统动态的更广泛，更精确的了解，并具有提高透明度和支持更公平决策的潜力。同时，与刑事司法有关的领域已经大大扩展了监视，数据生产和再利用，以及算法决策通常没有监督，认可或有关解决潜在问题的有效性的证据。刑事司法的数据技术基于分类和参与社会等级制度的新社会模式（从累犯风险评分到预测政策），对机会和生活机会产生了重要意义。我们的目标是开发用于连续摄入，集成和清洁结构化和非结构化数据的工具，以及对此类数据的分析。使用大型预训练的AI模型以及数据管理和人类计算机交互技术的组合，我们将开发工具来吸收来自各种政府和在线资源的信息，从而将其变成结构化数据进行分析。这些努力将为半自治和连续数据处理以及大规模整合提供新颖而可推广的工具，这些工具也保留了隐私并促进公平。NSFResearch训练（NRT）计划旨在鼓励开发和实施大胆的，新的潜在变革的STEM研究生教育培训模型。该计划致力于通过全面的跨学科或收敛性研究领域的STEM研究生进行有效培训，通过全面的培训模型，这些模型具有创新性，基于循证的，并且与不断变化的劳动力和研究需求保持一致。该奖项反映了NSF的法定任务，并通过使用基金会的知识优点和广泛影响来评估NSF的法定任务，并被视为值得的支持。