This project is a study of the use of artificial intelligence to assist police on patrol by using massive troves of historical crime data to train machine learning algorithms to anticipate the timing and location of criminal activity. Predictive policing has come under intense criticisms from civil rights groups, academics, and communities that have been subject to predictive policing. These criticisms include its recapitulation of racially biased patterns of policing, its further burdening of marginalized communities, and its infringement on the liberties of targeted communities. The researchers propose to examine these claims and to develop best practices for the development and deployment of algorithmic policing programs. The results of this project promise to benefit police departments, communities patrolled based on algorithmic crime predictions, and public understanding of the societal and ethical implications of predictive policing.
The aims of this grant are to examine and develop viable ethical frameworks for the assessment of predictive policing practices. In doing so, the researchers will propose and evaluate novel considerations that might bear on the ethics of predictive policing. They will also develop empirically grounded recommendations for the ethically sensitive and effective development and use of predictive policing. The results of this project promise to advance understanding and illuminate fertile new areas of research in moral philosophy, technology ethics, sociology, and criminology. They also will have clear relevance for other algorithmic technologies with similar implications for justice including algorithms that predict criminal sentences, make healthcare diagnoses, and serve as gatekeepers to government benefits (all of which are currently in use).
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.
