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

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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.