ICE Using ChatGPT To Write Use-Of-Force Reports, As Fascism Meets Laziness
The nexus of federal enforcement, rapidly evolving artificial intelligence, and documented allegations of excessive force has culminated in a significant judicial confrontation in late 2025. The revelation that agents within Immigration and Customs Enforcement (ICE) and Customs and Border Protection (CBP) utilized Generative Pre-trained Transformer (ChatGPT) to compose official Use-Of-Force (UOF) reports has cast a stark light on institutional accountability, raising profound questions about bureaucratic apathy and the automation of justification. This technological shortcut did not emerge in a neutral environment; it was brought to light within a fiercely contested legal battle concerning aggressive enforcement operations in the Chicago metropolitan area, an operation the presiding judge found deeply troubling and often unjustified.
Operational Context: Allegations of Excessive Force
The deployment of artificial intelligence for report writing cannot be divorced from the underlying activities it was intended to document. The entire controversy is inextricably linked to the severity and perceived lack of justification for the physical force utilized by federal personnel during operations targeting immigrant communities and associated protests.
The Backdrop of Enforcement Activities
The enforcement activities under judicial scrutiny, often characterized under the banner of “Operation Midway Blitz,” involved federal personnel employing maneuvers that critics and the court later deemed highly aggressive. The documentation reviewed by the court presented numerous high-profile clashes where the agents’ assertions of imminent danger seemed to wilt under the scrutiny of available video evidence, establishing a volatile climate where the integrity of any official report was already under intense pressure before the AI element was even revealed.
Specific Munitions Deployed Against Civilians
The judicial opinion meticulously recounted specific tactics employed by agents during these encounters. These included the deployment of chemical irritants such as tear gas, the firing of less-lethal munitions like pepper balls, and instances of direct physical contact, such as kicking individuals already on the ground. In several instances cataloged by Judge Sara Ellis, munitions were allegedly deployed without the requisite warnings or directed at individuals clearly attempting to retreat, such as when flash-bang grenades were reportedly hurled at the backs of fleeing persons. The narrative was further complicated by evidence suggesting internal antagonism, including reports of agents allegedly expressing glee over the prospect of deploying gas, which severely challenged the assertion that these actions were purely objective necessities of their duty.
Discrepancies Between Agent Testimony and Recorded Reality
A foundational finding of the judicial review was the recurring, systematic inconsistency, not just in the written submissions, but in the sworn testimony provided by supervisory personnel during the legal proceedings. This suggests a systemic challenge to the fidelity of the official record that long predates the introduction of any generative artificial intelligence system.
Case Studies in Contradictory Accounts
The judge cataloged specific contradictions that moved well beyond minor procedural errors. For example, governmental assertions that protesters were actively throwing objects or exhibiting violence were frequently contradicted by body-worn camera (BWC) footage showing agents themselves initiating or escalating physical confrontations. Another striking illustration involved claims regarding the identification of gang affiliations based solely on attire; the court found these assertions to be factually flawed or based on erroneous assumptions when cross-referenced with external data, such as one instance where an individual in a maroon hoodie was identified as a local official. These material discrepancies formed the essential basis for the argument that the government’s composite account of the events was, in the judge’s assessment, “simply not credible”.
Critique of High-Ranking Command Testimony
The judicial scrutiny of credibility was not confined to the rank-and-file agents; it extended unequivocally to the command staff. Border Patrol Commander Gregory Bovino, for instance, was singled out for particularly sharp criticism by the court, with the judge explicitly stating that his testimony during deposition was found to be “not credible” and characterized by answers deemed either “cute” or, indeed, “outright lying”. When the testimony of leadership is assessed as fundamentally unreliable by a court, the utility and integrity of any official report generated under that command structure—regardless of whether it was manually crafted or AI-assisted—is demonstrably diminished.
The Philosophical and Ethical Stance on Automation of Accountability
The utilization of ChatGPT in this particularly sensitive domain of documenting state-sanctioned force compels a confrontation with broader philosophical underpinnings regarding technology’s role in cataloging institutional failures and managing essential public trust. The prevailing view posited by the court’s findings is that the issue transcends a mere “AI problem”; it is, fundamentally, a reflection of a pre-existing organizational culture.
The Concept of Delegated Laziness in Bureaucracy
Commentary surrounding this incident frequently characterized the AI use as the ultimate expression of “delegated laziness,” suggesting that agents actively sought to circumvent the “pesky paperwork” associated with detailing potentially controversial actions, even when such documentation is a non-negotiable mandate under the law. This points toward a significant procedural apathy where the fundamental, core responsibility of justifying the application of force is deemed too burdensome, leading to the outsourcing of this vital task to an algorithm. The pronounced irony highlighted in the initial reports was the stark juxtaposition of agents expending considerable physical effort to confront citizens, while simultaneously outsourcing the minimal administrative duty of accurately reporting that very confrontation.
The Broader Societal Implications of Outsourcing Duty
This specific episode is framed by analysts as a microcosm of a pervasive, larger trend: the uncritical belief that technology must be applied universally to every task, even those fundamentally unsuited to its current capabilities, particularly tasks demanding high-level ethical judgment and absolute factual precision. Applying a “stochastic parrot”—a term used to describe large language models prone to generating plausible but fabricated content—to matters of civil rights and documented use of force suggests a dangerous institutional willingness to permit algorithmic distance between an action and its required constitutional justification. This dynamic is often viewed as dystopian, as it serves to introduce a veneer of artificial eloquence over conduct that may be legally questionable or brutally executed, thereby erecting an additional, technological barrier to public accountability.
Legal Ramifications and The Fate of the Initial Order
The judicial order stemming from Judge Ellis’s investigation immediately triggered significant legal consequences, prompting rapid, albeit temporary, action from higher courts which moved to suspend the order’s sweeping provisions.
Immediate Appellate Response to the Injunction
Shortly following Judge Ellis’s issuance of her comprehensive preliminary injunction—which sought to prohibit certain riot-control methods unless force was deemed “objectively necessary to stop an immediate threat”—an appellate panel intervened. This appellate court voted swiftly to block the injunction, concluding that its broad application to the federal government was “overbroad” and “too prescriptive”. Critically, while the appeals court did not overturn the district court’s specific factual findings regarding agent misconduct, their action placed the immediate, restrictive limitations on agent conduct on hold, pending further substantive arguments. The procedural posture of the case thus shifted dramatically, even as the damaging factual revelations regarding both conduct and AI use remained firmly in the public domain. Oral arguments on the substantive case were scheduled for December 17, 2025.
Implications for Future Use-Of-Force Protocols
Irrespective of the immediate procedural fate of the injunction, the documented findings, particularly the AI usage, will undoubtedly exert a lasting influence on future legal challenges and internal agency mandates. The judge’s conclusion that the use of ChatGPT “further undermines their credibility” establishes a novel and potent area of evidence that plaintiffs and defense counsel alike must now address when assessing the reliability of federal documentation. Future internal audits or court mandates are likely to require specific disclosures concerning the utilization of any generative AI in narrative reports, fundamentally altering the compliance landscape for official documentation moving forward.
Analysis of Underlying Agent Conduct Preceding AI Adoption
A crucial element in analyzing this entire episode is the insistence that the technological shortcut did not *cause* the foundational issues; rather, it appears to have been grafted onto a system already characterized by questionable operational conduct. The AI was demonstrably not the catalyst for the alleged misconduct, but instead, it became a potential accelerant for obfuscation and narrative control.
Evidence of Systemic Issues Beyond Technology Misuse
The established judicial record demonstrates a history of alleged abuses—ranging from confrontations during holiday celebrations to aggressive actions against veterans—that occurred entirely irrespective of whether an agent possessed access to a generative AI chatbot. The established pattern of behavior, which included allegedly providing false statements about perceived threats, employing excessive force without proper protocol, and exhibiting open antagonism toward the public, was demonstrably in place before the AI-generated reports were even introduced as evidence. This strongly indicates that the problematic conduct is deeply rooted in training deficiencies, entrenched cultural norms, or command philosophy, rather than being solely attributable to technological dependency.
The Irony of Mechanizing a Pre-Existing Cultural Deficit
The ultimate irony observed by legal commentators is the agency’s decision—which was already allegedly engaged in actions that “shocked the conscience”—to then rely upon an algorithm to articulate and legally defend those very actions. To deploy a tool inherently prone to fabrication to justify conduct already proven dubious by direct visual evidence highlights a profound and perhaps irreconcilable disconnect between the agency’s perception of its constitutional duties and the actual standards it is sworn to uphold. The mechanization of the report-writing process is therefore interpreted by many as the logical, if deeply dystopian, endpoint of a pre-existing cultural deficit in diligence and genuine accountability. This entire episode serves as a potent, and concerning, development in the continuous public discourse surrounding the intersection of artificial intelligence in government operations and the bedrock principles of civil liberties.
As other law enforcement agencies, such as departments in Utah, have begun integrating AI tools like Peach Safety for report writing, often citing significant time savings and accuracy improvements, the ICE/CBP case presents a critical counter-narrative. While some agencies mandate disclaimers and officer certification of accuracy, the Chicago case demonstrates how the temptation to delegate the “effort” of justification can lead to a systemic erosion of credibility when that AI is applied to document actions already marked by aggressive overreach. The conversation in late 2025 is thus less about whether AI can *assist* police work and more about the ethical guardrails required when AI is asked to *absolve* it.
