How Race and Ethnicity Shape Modern Surveillance
Mass surveillance often targets marginalized communities without evidence.
In an era defined by rapid technological advancement and omnipresent data collection, the conversation surrounding privacy often focuses on corporate data harvesting or foreign espionage. However, a far more pressing domestic issue remains obscured by the rhetoric of national security: the targeted surveillance of marginalized communities by local, state, and federal law enforcement agencies. For decades, intelligence gathering has frequently bypassed the fundamental requirement of suspected criminal activity, opting instead to monitor individuals based on their race, ethnicity, religion, or political affiliation.
This systemic issue is not a relic of a bygone era but a modernized, highly sophisticated operation. From the deployment of predictive policing algorithms to the use of suspicionless federal “assessments,” the modern surveillance apparatus continuously reflects and amplifies historical prejudices. When the government decides who to watch, the answer often depends heavily on the color of their skin or the religion they practice. Understanding the mechanisms of this racialized surveillance is essential for defending civil liberties and ensuring equal protection under the law.
The “Assessment” Loophole: Spying Without Suspicion
The foundation of any just legal system is the presumption of innocence and the requirement of probable cause and the requirement of probable cause before the state can invade an individual’s privacy. However, policy shifts following the September 11 attacks drastically lowered the threshold for initiating an investigation, granting sweeping new powers to intelligence agencies.
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One of the most consequential changes was the expansion of the Federal Bureau of Investigation’s (FBI) authority to conduct “assessments.” Unlike traditional preliminary or full investigations, which require an objective factual basis that a crime has been or is being committed, an assessment requires absolutely no factual predicate. Law enforcement agents can initiate these inquiries based on unverified tips, generalized threats, or even arbitrary discretion.
During an assessment, federal agents are permitted to employ highly intrusive techniques. They can dispatch confidential informants to infiltrate community organizations, conduct covert physical surveillance, and search comprehensive commercial and law enforcement databases. Because there is no requirement to demonstrate criminal suspicion, these tools are frequently turned against ethnic and religious minorities. This policy loophole essentially legalizes racial profiling, allowing agencies to map demographic enclaves and treat entire communities as suspect populations under the guise of proactive threat detection.
Collateral Damage: Spying on Muslim American Communities
The disproportionate impact of suspicionless surveillance is perhaps most starkly illustrated by the sustained monitoring of Arab and Muslim American communities over the past two decades. In the name of counterterrorism, federal and local agencies have often treated the basic exercise of faith and cultural identity as potential indicators of radicalization.
A glaring historical example of this overreach was the New York Police Departments (NYPD) Demographics Unit. Operating in secrecy for years, this unit dedicated itself to mapping neighborhoods predominantly inhabited by Muslim families. The NYPD deployed undercover officers and informantsoften referred to as “mosque crawlers”to monitor daily life in mosques, student associations, halal butcher shops, and cafes. They documented mundane conversations, recorded the license plates of worshipers, and cataloged the innocent activities of citizens who had absolutely no connection to terrorism.
According to comprehensive reviews, including foundational reports from the Brennan Center for Justice, this sprawling surveillance program did not generate a single actionable terrorism lead. It did, however, succeed in inflicting severe psychological and communal damage. The knowledge that their sacred spaces and community hubs were infiltrated by the state created a profound chilling effect. Members of the community self-censored their political speech, altered their religious attire, and became deeply suspicious of one another, tearing at the very fabric of community trust.
Monitoring Black Activism: From COINTELPRO to the Modern Era
The surveillance of Black Americans is a deeply entrenched practice that predates modern intelligence agencies, rooted in historical efforts to suppress civil rights movements. While the infamous COINTELPRO operations of the 1960swhich aggressively monitored and disrupted leaders like Dr. Martin Luther King Jr. and the Black Panther Partyare widely condemned today, the underlying philosophy of those programs persists.
In recent years, the rise of the Black Lives Matter movement and widespread protests against police brutality have been met with intense, technologically advanced surveillance. Law enforcement agencies have utilized joint terrorism task forces to monitor activists, treating nonviolent civil disobedience as a potential national security threat.
Todays surveillance of Black communities extends far beyond human informants. Police departments routinely deploy automated license plate readers, cell-site simulators (often known as Stingrays), and advanced facial recognition software at protests and in predominantly Black neighborhoods. The U.S. Commission on Civil Rights has explicitly noted that facial recognition technology suffers from severe demographic biases, demonstrating significantly higher false-positive rates for Black individuals, particularly Black women. When this flawed technology is deployed in over-policed communities, it directly results in wrongful arrests, further eroding trust in law enforcement and subjecting innocent people to the trauma of the criminal justice system.
The Illusion of Neutrality: Algorithms and Predictive Policing
As law enforcement agencies face public pressure to modernize and eliminate bias, many have turned to “predictive policing”the use of artificial intelligence and machine learning algorithms to forecast where crimes will occur or who will commit them. Proponents argue that relying on data removes human prejudice from policing, offering an objective, mathematical approach to public safety.
However, predictive policing is far from neutral. Algorithms are trained on historical crime data, which does not reflect an objective reality of where crimes happen, but rather a historical record of where police have historically enforced the law. Decades of targeted enforcement, discriminatory stop-and-frisk policies, and racial profiling have generated massive datasets that heavily skew toward low-income neighborhoods and communities of color.
When this “dirty data” is fed into a predictive algorithm, the software predictably instructs officers to return to those exact same marginalized neighborhoods. This creates a destructive feedback loop: the algorithm sends police to a minority community, police make arrests for low-level offenses, the new arrest data validates the algorithm’s prediction, and the cycle repeats. Research from institutions such as Yale Law School and state policy laboratories has repeatedly highlighted how these models compound implicit biases under the veneer of technological objectivity, effectively laundering racial prejudice through code.
The Erosion of First and Fourth Amendment Rights
The targeted surveillance of marginalized groups creates a silent constitutional crisis. The Fourth Amendment is designed to protect citizens from unreasonable searches and seizures, demanding that the state establish probable cause before invading an individual’s private life. Yet, the mass collection of data, the deployment of biometric tracking in public spaces, and the use of suspicionless assessments continually bypass these constitutional safeguards.
Equally troubling is the assault on First Amendment freedoms. The freedoms of speech, assembly, and religion are severely compromised when citizens know the government is watching them based on their demographic identity. When attending a mosque or participating in a peaceful protest makes an individual the subject of an intelligence file, the immediate result is self-censorship. People disengage from civic life, abandon community organizing, and hide their religious identities to avoid state scrutiny. A healthy democracy cannot function when a substantial portion of its populace lives under the constant, unwarranted gaze of the state.
Charting a Course Toward Accountability
Dismantling the architecture of racialized surveillance requires comprehensive legislative reform and robust community oversight. First and foremost, the authority of federal agencies to conduct suspicionless assessments must be revoked. Investigations should only be permitted when there is a clear, factual, and articulable suspicion of criminal wrongdoing, ensuring that the government cannot target individuals based solely on First Amendment-protected activities or demographic traits.
At the state and local levels, lawmakers must impose strict moratoriums on the use of fundamentally flawed technologies like facial recognition in law enforcement contexts. Furthermore, any deployment of new surveillance technology, including predictive policing software, must be subject to democratic approval. Communities must have the right to review the technology, understand the algorithms, and ultimately vote on whether such tools should be permitted in their neighborhoods.
Conclusion
The question of who is subjected to government surveillance in the United States is inextricably linked to race, ethnicity, and religion. For marginalized communities, the surveillance state is not an abstract dystopian concept, but a daily reality that curtails their freedoms and threatens their safety. True security cannot be achieved by sacrificing the civil liberties of minorities on the altar of intelligence gathering. Moving forward, society must demand an intelligence apparatus that operates within the strict bounds of the Constitution, prioritizing equal justice and individual liberty over suspicionless, racialized data collection.
Frequently Asked Questions
What is an FBI “assessment”?
An assessment is a type of federal investigation that allows FBI agents to look into individuals or groups without requiring any factual basis or suspicion of a crime. This low threshold permits the use of informants and physical surveillance, which civil rights advocates argue facilitates racial and religious profiling.
How does predictive policing reinforce racial bias?
Predictive policing algorithms use historical arrest data to forecast future crimes. Because marginalized communities have historically been over-policed, the data heavily focuses on these areas. The algorithm then sends officers back to the same neighborhoods, creating a biased feedback loop of continuous enforcement.
What was the NYPD Demographics Unit?
The NYPD Demographics Unit was a secretive intelligence division that actively mapped and spied on Muslim neighborhoods, mosques, and businesses in the years following 9/11 without any evidence of criminal wrongdoing. The program was heavily criticized for violating civil rights and was ultimately disbanded.
References
- The Civil Rights Implications of the Federal Use of Facial Recognition Technology – U.S. Commission on Civil Rights. 2024-09-19. https://www.usccr.gov/files/2024-09/civil-rights-implications-of-federal-use-of-facial-recognition-technology.pdf
- American Jihadist Terrorism: Combating a Complex Threat – Congressional Research Service. 2014-02-19. https://digital.library.unt.edu/ark:/67531/metadc283457/
- Algorithms in Policing: An Investigative Packet – Yale Law School. https://law.yale.edu/sites/default/files/area/center/mfia/algorithms_in_policing_investigative_packet.pdf
- National Security and Local Police – Brennan Center for Justice. 2011-08-23. https://www.brennancenter.org/our-work/research-reports/national-security-and-local-police
- Predictive Policing: A Digital Pandora’s Box – New Jersey State Policy Lab, Rutgers University. 2026-01-29. https://policylab.rutgers.edu/predictive-policing-a-digital-pandoras-box/
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