Data, Society and Justice Initiative
We live in a much-needed period of awakening when it comes to the racial and economic disparities that mar the United States’ criminal justice system. However, the issue of mass incarceration is rarely framed as a data problem. The Data, Society and Justice Initiative responds to the need to further understand the role of criminal justice data, while working toward a future that reimagines and contextualizes this data.
To date, the methods and practices for studying these discrepancies have largely under-accounted for social, political and economic forces. The vast majority of research in the field of criminology focuses solely on the arrested individual. This initiative seeks to understand this data and reimagine it in a more profound way. When collected and analyzed, this data can help identify the problems that matter to a community. It can shape how these problems are addressed and can help us assess the impact of interventions.
This initiative is the brainchild of Ariel Ludwig, Ph.D., visiting assistant professor with University of Houston’s Data and Society program. I had the privilege of interviewing Ludwig to better understand the connection she sees between data, society and justice.
Julia Chamon: Thank you for allowing me to interview you, Professor Ludwig! Can you give me a little background on your career and research, up until now?
Pr. Ariel Ludwig: My work and research has primarily surrounded the intersection of health and mass incarceration. Before pursuing a Ph.D., I had extensive work experience both in correctional facilities and non-profit organizations that assisted people released from incarceration and their families. Across my career, I have come to see mass incarceration as one of the most important civil rights issues of our time.
JC: Where do data science and the mass incarceration epidemic in the U.S. cross paths?
AL: Data science intersects with mass incarceration at nearly every point. For instance, even prior to a person’s arrest, predictive policing may guide allocation of police resources and time. On the other side of the process, algorithms are used to inform parole decisions and monitoring practices. While data science has often served to shore up existing practices and disparities, it is possible to use them to work toward a just and compassionate future.
JC: What will the Data, Society and Justice Initiative be focusing on in terms of research? Will it involve criminal profiling, or something else?
AL: The Data, Society and Justice Initiative aims to work against the disparities and structural violences that have made the criminal justice system what it is today. To date, the methods and practices have largely under-accounted for social, political and economic forces. For instance, the vast majority of research in the field of criminology focuses solely on “criminals.” Data that is attentive to social context and existing inequities will enable better outcomes and a clearer understanding of the justice system. It is from this site that the Data, Society and Justice Initiative can intervene, while simultaneously responding to the interest of students and the community. For instance, the Data, Society and Justice Initiative has begun to partner with several nonprofits and is in the process of developing a reentry initiative modeled on the Community Health Worker Initiative.
JC: Where does coalition building come into play? Have you made any notable partnerships within the community?
AL: Coalition building is vitally important to ending mass incarceration. We have already begun building partnerships with local community organizations that work to address the criminal justice system from trial to reentry. Data has the potential to bring diverse organizations together as improved metrics of success are established and data silos are removed. In bringing these organizations to the table, the University of Houston can assist in building a coalition and serve as a resource and model for other cities in Texas and beyond.
JC: How can bias be reflected in statistics in ways people don't necessarily expect?
AL: Some of the most insidious forms of bias surround: 1) the way that we define and conceptualize variables and categories, 2) assumptions of causality rather than correlation and 3) the design of questions and the assumptions that they are premised upon.
JC: Do you see the potential for educational opportunities to come out of this initiative?
AL: There are numerous educational opportunities arising from this initiative. While some of this takes places within the context of the classroom, it also expands beyond this. For instance, in the fall we will begin incorporating a learning-by-doing module in our 3350 course that develops a platform to meet the needs of the Juvenile Public Defender’s Office to use during grand jury hearings in order to reduce disparities. Students will be invited to work on this project across the semester. In the spring, we will offer a course addressing the history and politics of carceral data as students work on projects that aim to better represent and articulate justice data.
JC: Oftentimes, the rhetoric and even the visualization of data can be difficult for people to understand. How will you make the discoveries you find understandable to those with little understanding of data science?
AL: Data science is often intimidating and can seem impenetrable. This makes it of vital importance that rhetoric and visualization of data is accessible. It is also vital that the communities studied are engaged in the research and analysis processes. The University of Houston has undertaken vital community work surrounding health, nutrition and education. Now, this important work will be expanded further to the criminal justice system and its impact on communities and communities of color, in particular.