Police departments in some of the largest U.S. cities have been experimenting with predictive policing as a way to forecast criminal activity. Predictive policing uses computer systems to analyze large sets of data, including historical crime data, to help decide where to deploy police or to identify individuals who are purportedly more likely to commit or be a victim of a crime.
Proponents argue that predictive policing can help predict crimes more accurately and effectively than traditional police methods. However, critics have raised concerns about transparency and accountability. Additionally, while big data companies claim that their technologies can help remove bias from police decision-making, algorithms relying on historical data risk reproducing those very biases.
Predictive policing is just one of a number of ways police departments in the United States have incorporated big data methods into their work in the last two decades. Others include adopting surveillance technologies such as facial recognition and social media monitoring. These developments have not always been accompanied by adequate safeguards.
This mini toolkit is intended to guide left movement groups through building a risk assessment for an event, action, or...
This report—which relies on an extensive literature review and interviews with prosecutors around the country—begins to catalog current AI uses...
This is a policy framework for police use of robots, including ground robots and unmanned aerial vehicles (“UAVs”), also known...
We showcase existing solutions and new research from the community. Support us by sharing your own resources to our hub.
