Resources

This essay is part of the ongoing “AI Lexicon” project, a call for contributions to generate alternate narratives, positionalities, and understandings to the better known and widely circulated ways of talking about artificial intelligence (AI).

This report delves into the rhetoric of “smart borders” to explore their ties to a broad regime of border policing and exclusion that greatly harms migrants and refugees who either seek or already make their home in the United States. Investment in an approach centered on border and immigrant policing, it argues, is incompatible with the realization of a just and humane world.

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.

Institutional violence within community centers, healthcare organizations, and social services, in concert with the “helping” industry’s increasing collusion with and reliance on law enforcement, fuels the prison pipeline. In response to pervasive institutional violence and increasing policing, surveillance, and targeting of queer and TGNB (trans and gender non-binary) youth of color, street-based youth, and youth experiencing homelessness, Project NIA created a toolkit to share strategies of resistance to the increased securitization of non-profit spaces.

Over the last three and a half years, the City of Detroit has greatly expanded Project Green Light, an initiative of the Detroit Police Department (DPD), along with local businesses and other organizations, to use video surveillance and digital technology to fight crime. Since the first cameras went live in eight gas stations on January 1, 2016, the system has grown as of April 2020 to nearly 700 locations across the city.

Though it is billed by proponents as a “real-time crime-fighting” solution, others, including the DSA, see it as a mass-surveillance system that disproportionately singles out communities of color. In particular, critics cite flaws in the technology behind the project that are part of what sociologist Ruha Benjamin, in her study Race After Technology, terms the “New Jim Code.”

In this report, OIG details ShotSpotter’s functionality and descriptive statistics regarding law enforcement activity related to CPD’s response to ShotSpotter alerts. OIG does not issue recommendations associated with this descriptive data. OIG is issuing this analysis of the outcomes of ShotSpotter alerts to provide the public and City government officials—to the extent feasible given the quality of OEMC and CPD’s data—with clear and accurate information regarding CPD’s use of ShotSpotter technology.

OIG concluded from its analysis that CPD responses to ShotSpotter alerts rarely produce documented evidence of a gun-related crime, investigatory stop, or recovery of a firearm. Additionally, OIG identified evidence that the introduction of ShotSpotter technology in Chicago has changed the way some CPD members perceive and interact with individuals present in areas where ShotSpotter alerts are frequent.

Defund. Re-Envision. Transform. is a grassroots campaign anchored by Action St. Louis, CAPCR, Forward Through Ferguson, and ArchCity Defenders, which demands the defunding of the St. Louis Metropolitan Police Department (SLMPD), the re-envisioning public safety through reinvestment into community resources that actually keep our communities safe, and transformation of the St. Louis region.

Sitting at the intersection of criminalization and capitalism, the use of emerging surveillance technology has become increasingly popular among police departments in the United States over the last few decades. While public knowledge is still catching up to the full extent of the tools that police use, we are quickly understanding more about this technology each day. Adopted for use as police “reforms,” sophisticated electronics and tech capabilities do not address the unchecked power and ballooning budgets of local police departments. Instead, they open the door for law enforcement to monitor communities while private companies profit from sales and contracts. As the movement to defund the police becomes impossible to ignore, replacing police officers with police cameras is called progress.

Living in a “surveillance state,” however, is not a foregone conclusion. Organizers across the country are pushing back against intrusive and problematic surveillance technologies by providing program models and model legislation to disrupt 21st Century Policing and ensure awareness and meaningful interventions. This report presents an overview of ongoing trends in police surveillance and the funding streams that have made and continue to make these trends possible. It also highlights ongoing advocacy efforts and provides recommendations for pushing back against the use of such technology by law enforcement.

HRDAG’s analysis and expertise continues to deepen the national conversation about police violence and criminal justice reform in the United States. In 2015 we began by considering undocumented victims of police violence, relying on the same methodological approach we’ve tested internationally for decades. Shortly after, we examined “predictive policing” software, and demonstrated the ways that racial bias is baked into the algorithms. Following our partners’ lead, we next considered the impact of bail, and found that setting bail increases the likelihood of a defendant being found guilty. We then broadened our investigations to examine the risk assessment tools that judges use to make decisions about pre-trial supervision, and we found evidence of racial bias in the tools. Most recently we have returned to considering the challenges of documenting police violence.