Understanding the Causal Effect of Race in the Criminal Justice System
This project looks at how to model uncertainty and come up with causally identified inferences involving ML techniques. In the precedent work, the research proposed a systematic framework to causally understand the effect of race on policing, enabling the characterization of racial disparities in law enforcement. Applying this framework, the team will analyze the linked data quantitatively to estimate how different races benefit or suffer differently from the same policy interventions, e.g., legalization of recreational marijuana, use of body cameras, implementation of face recognition systems, etc. Moreover, they will coordinate a randomized controlled trial (RCT), possibly in a virtual reality setting, which will allow us to directly test some of the tools we have been building. The data result of the RCT will be made available through the open data initiative, which will likely fuel exciting research as well as teaching activities at the interface of computer science and social sciences.