What looks like randomness in modern immigration enforcement is not disorder – it is design. Across the country, U.S. Immigration and Customs Enforcement (ICE) and Border Patrol are no longer primarily hunting for specific individuals accused of violating immigration law. Instead, they are targeting places – neighborhoods, parking lots, workplaces, apartment complexes, and now protest sites – where data systems suggest undocumented immigrants might be found.
In doing so, federal agents have constructed an enforcement model in which proximity replaces suspicion, probability substitutes for proof, and constitutional protections erode not through open defiance, but through algorithmic indirection.
The result is a growing pattern of seemingly arbitrary arrests and detentions that has swept up undocumented workers, lawful visa holders, and U.S. citizens, while quietly expanding into the monitoring of political protest and dissent.
At the center of this shift is the convergence of two systems that were never designed to operate together on American streets: probabilistic geospatial targeting tools such as ICE’s Enhanced Leads Identification & Targeting for Enforcement platform (ELITE), and mobile biometric identification tools like Mobile Fortify.
Together, they form an enforcement architecture that no longer requires agents to know exactly who they are looking for, or where that person is likely to be found. Instead, agents are directed toward “target-rich” locations like neighborhoods, apartment complexes, parking lots, and agricultural work sites where undocumented immigrants may statistically be present, even if no specific individual has been identified in advance.
This is not an incidental byproduct of modern policing. It is the logical outcome of how ICE has re-engineered enforcement decision-making around probability rather than proof.
ELITE operates as a geospatial interface layered atop ICE’s broader Palantir Technologies analytics ecosystem, converting fused administrative records into map-based deployment strategies.
Rather than simply retrieving records about known individuals, ELITE allows officers to draw geographic boundaries and surface multiple potential “targets” at once, assigning each a numerical confidence score that reflects how likely ICE believes that person is to be located at a given address.
Crucially, those same scores are also used to identify areas where multiple viable targets appear to cluster, allowing enforcement resources to be concentrated for maximum yield.
In practice, this means that enforcement planning increasingly begins not with a person, but with a place. That distinction matters. A confidence score does not establish that a particular individual is present at a particular location at a particular time. It establishes only statistical likelihood.
Courts have never held that such probabilistic assessments satisfy the Fourth Amendment’s requirement for individualized probable cause. But ICE’s enforcement model is not designed to test that proposition in court.
Instead, ELITE’s probabilistic outputs are used to guide where agents position themselves in public and quasi-public spaces where constitutional protections are weaker and judicial warrants are not required.
Administrative arrest warrants, signed by an ICE deportation official rather than by judges, further insulate the system from external scrutiny. If agents never seek a judicial warrant to enter a home, they never have to explain or defend the reliability of the data that led them there in the first place.
This structural avoidance of judicial review helps explain why area-based enforcement has become so central. It is not merely operationally efficient; it is legally expedient.
By focusing on locations where people are statistically likely to emerge, rather than attempting to prove that a specific person is inside a specific residence, ICE sidesteps the constitutional demand for certainty and replaces it with a logic of deployment.
The consequences of that shift became starkly visible during the October 2025 raids in Woodburn, Oregon, part of a sweeping enforcement effort internally referred to as Operation Black Rose.
Testimony has revealed that agents from across the country were flown into Oregon and assigned daily arrest quotas, with each arrest team expected to apprehend eight people per day. Multiple teams operating simultaneously meant that enforcement success was measured not by accuracy, but by volume.
Agents described receiving “target packages” that sometimes lacked names altogether. In some cases, the “target” was a license plate. In others, it was simply an area.
One agent testified that his team’s focus on the day of the raid was not a particular individual, but a location described as “target-rich,” meaning Department of Homeland Security (DHS) data suggested that people with an “immigration nexus” were likely to be present.
Live targeting followed, with license plates run through DHS databases in real time, and vehicles stopped within minutes of a purported match.
It was in this context that “MJMA,” a farmworker with a valid B-2 visa who was seeking asylum, was swept up alongside more than 30 others. When she declined to answer questions – a right she indisputably held – a Customs and Border Protection (CBP) official used Mobile Fortify to scan her face.
The app returned one name, then another, both incorrect. Agents nonetheless attempted to interrogate her based on those results before ultimately releasing her the next day without conditions.
The misidentification itself is alarming, but it is the system’s response to error that is most revealing. There was no automatic halt, no audit, no requirement to purge the biometric data collected from her face, and no consequence for relying on demonstrably false matches.
Mobile Fortify and ELITE function as one-way systems. Data is captured, retained, and reused, while the costs of error are borne entirely by the individuals subjected to enforcement.
This asymmetry helps explain why ICE has been able to treat Mobile Fortify matches as “definitive” while simultaneously disclaiming any obligation to quantify the tool’s error rate.
In testimony, an ICE deportation officer admitted he could not speak to the app’s rate of identification success. Yet ICE has told lawmakers that officers may disregard documentary evidence of citizenship, including birth certificates, if the app indicates otherwise.
When enforcement decisions are guided by probabilistic assessments rather than concrete evidence, randomness is not a bug, but rather it is a feature. People are detained not because agents know who they are, but because they happen to be in a place the data has targeted as fertile ground for arrests. U.S. citizens, lawful residents, and undocumented immigrants become interchangeable inputs in a system optimized for throughput.
That same logic now appears to be extending beyond immigration enforcement and into the realm of political expression. This past week, border czar Tom Homan stated publicly that protestors are being monitored, remarks that coincided with reports out of Minneapolis that demonstrators returned home to find federal agents waiting for them.
While officials have attempted to frame such encounters as incidental to “targeted enforcement,” the underlying infrastructure tells a different story.
The same tools that identify “target-rich” neighborhoods for immigration raids can just as easily identify protest locations. The same license plate databases used to flag vehicles near agricultural sites can flag cars parked near demonstrations. Facial recognition systems designed to resolve identity in the field can re-identify protestors after the fact.
Once identities are resolved, addresses are trivial to obtain.
This is not mission creep so much as mission portability. An enforcement architecture built around continuous data ingestion, geospatial analysis, and probabilistic targeting does not care why a person is interesting, only that they are locatable.
When federal officials openly acknowledge monitoring protest activity, the implication is clear. Immigration enforcement tools are now being repurposed for domestic political surveillance, even though they were never authorized or debated for that use.
Historically, the dangers of such approaches are well documented. “High-crime area” policing, stop-and-frisk, fusion center overreach, and COINTELPRO all relied on similar logics of proximity and association. In each case, courts and the public eventually recognized that substituting probability for individualized suspicion leads to systemic rights violations.
What distinguishes the current moment is scale and automation. ELITE and Mobile Fortify do not merely assist human judgment; they reshape it. They encourage officers to think in terms of yield rather than justification, to view neighborhoods as enforcement opportunities, and to treat uncertainty as acceptable collateral damage.
Arrest quotas reinforce that mindset by converting enforcement into a numbers game, where stopping the wrong person still counts as success so long as the count increases.
The chilling effect is already visible. If lawful status does not protect a person from detention and proximity alone is enough to justify questioning, then compliance becomes impossible.
Communities learn that avoiding enforcement is not about following the law, but about avoiding visibility altogether. Protestors learn that civic participation may trigger surveillance, and fear becomes a tool of governance.
Taken together, these developments represent more than aggressive immigration enforcement. They mark a structural shift toward a domestic surveillance model in which probability substitutes for proof and location substitutes for suspicion.
The randomness that now defines ICE and Border Patrol encounters is not accidental. It is the predictable outcome of an enforcement system that has been deliberately redesigned to operate without judicial friction, without transparency, and without meaningful mechanisms for correction.
Once normalized, such a system does not remain confined to immigrants. It becomes a template that can be applied to protestors, activists, journalists, and ultimately anyone who happens to be standing in the wrong place at the wrong time, according to an algorithm that no court has ever approved.
Article Topics
accuracy | biometric matching | biometrics | DHS | facial recognition | ICE | identity verification | immigration | Mobile Fortify | surveillance | U.S. Government
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