Artificial intelligence has entered immigration law faster than regulation can keep up.
In the past 18–24 months, we have seen a dramatic increase in clients who:
AI-generated evidence in immigration cases has become increasingly prevalent.
Many are now exploring the implications of AI-generated evidence in immigration cases.
The use of AI-generated evidence in immigration cases raises unique challenges.
AI-generated evidence in immigration cases is designed to enhance documentation.
The appeal is obvious: speed, fluency, structure, confidence.
But immigration law is not a writing exercise.
Scrutiny of AI-generated evidence in immigration cases is increasing.
It is a credibility-driven adjudicative system.
And we are now entering a phase where AI-generated uniformity intersects directly with established fraud and credibility doctrine.
Understanding the role of AI-generated evidence in immigration cases is crucial for legal practitioners navigating this landscape.
The issue is no longer theoretical.
It is litigated.
Learn more below and in our short video
Many people believe AI creates a new legal problem.
Gathering AI-generated evidence in immigration cases is not a new challenge.
It doesn’t.
The doctrine was already there.
AI-generated evidence in immigration cases can lead to complexities in legal arguments.
In Matter of R-K-K-, 26 I&N Dec. 658 (BIA 2015), the Board of Immigration Appeals held that an immigration judge may rely on “significant similarities between statements submitted by applicants in different proceedings” to support an adverse credibility finding.
This is critical.
The BIA did not require proof of plagiarism software.
It did not require proof of collusion.
It did not require proof of AI use.
The implications of AI-generated evidence in immigration cases must be addressed.
It allowed similarity itself — when significant — to become part of the credibility calculus.
The safeguards required:
Judges increasingly assess AI-generated evidence in immigration cases.
Challenges surrounding AI-generated evidence in immigration cases persist.
But the core doctrine is now settled law.
Similarity can be litigated.
Multiple federal circuits have examined cases where:
AI-generated evidence in immigration cases is scrutinized rigorously by courts.
The risks associated with AI-generated evidence in immigration cases are significant.
Courts have recognized that:
Understanding AI-generated evidence in immigration cases is crucial for all parties involved.
This doctrine predates generative AI.
AI simply multiplies the risk of linguistic convergence.
AI-generated evidence in immigration cases may affect decision-making processes.
The implications of AI-generated evidence in immigration cases cannot be overstated, as they present both challenges and opportunities.
Now we turn to something that is often misunderstood.
Public reporting and academic research describe a USCIS system known as Asylum Text Analytics (ATA) — designed to detect duplicate or plagiarized language across asylum filings.
The system reportedly:
The role of AI-generated evidence in immigration cases is evolving.
This matters because it demonstrates that:
AI-generated evidence in immigration cases highlights the need for vigilance.
The immigration system has already operationalized text comparison.
Even if ATA is used primarily at the affirmative asylum stage, the principle is established:
Narrative similarity is measurable.
Legal standards for AI-generated evidence in immigration cases are still developing.
Attorneys from U.S. Immigration and Customs Enforcement, within the Office of the Principal Legal Advisor (OPLA), operate within enterprise-level litigation ecosystems.
ICE has historically used advanced eDiscovery platforms (including Relativity and later Casepoint) capable of:
Understanding the nuances of AI-generated evidence in immigration cases is essential.
AI-generated evidence in immigration cases offers significant advantages but also risks.
No public rule says:
Judges will scrutinize AI-generated evidence in immigration cases closely.
“ICE runs plagiarism software on asylum declarations.”
But the infrastructure to compare documents exists.
And the legal doctrine to use similarities in court exists.
That intersection is what matters.
AI-generated evidence in immigration cases is increasingly common.
Generative AI systems are trained on patterns.
They produce:
Legal professionals must navigate AI-generated evidence in immigration cases carefully.
AI-generated evidence in immigration cases requires thorough examination.
Consideration of AI-generated evidence in immigration cases is vital for applicants.
Example pattern AI often produces in asylum declarations:
Challenges associated with AI-generated evidence in immigration cases must be addressed.
The complexities of AI-generated evidence in immigration cases require careful analysis.
AI-generated evidence in immigration cases may shape future regulations.
That structure is not illegal.
Legal practitioners must adapt to the rise of AI-generated evidence in immigration cases.
But if dozens of unrelated cases contain:
The implications of AI-generated evidence in immigration cases are profound.
Pattern recognition becomes easier.
And under R-K-K-, similarity is admissible as part of credibility analysis.
We are seeing government counsel argue:
The argument is framed as:
Even when AI is not mentioned explicitly, the effect is similar.
Similarity becomes suspicion.
Suspicion becomes credibility damage.
Under the REAL ID Act, adjudicators may consider:
When similarity is introduced:
And here is the critical appellate reality:
Credibility findings are reviewed under a highly deferential standard.
Once credibility is damaged, reversal is difficult.
We are seeing RFEs referencing:
AI often produces phrases like:
If multiple waiver filings contain identical phrases, pattern scrutiny follows.
Hardship cases demand evidentiary integration.
AI cannot:
Under Matter of Dhanasar, NIW cases require precise evidentiary framing.
AI hallucination risk includes:
Misrepresentation — even unintentionally generated — carries permanent inadmissibility consequences.
There is no public USCIS rule stating:
“We use AI detectors.”
But detectability does not require AI detection software.
Red flags include:
Experienced adjudicators see patterns daily.
Uniformity is visible.
Under ABA Model Rule 1.1 (Competence):
Lawyers must understand the technology they use.
Under Rule 5.3:
Lawyers must supervise nonlawyer assistance — including AI tools.
Blind reliance on AI risks:
At Herman Legal Group, AI may assist brainstorming — but:
Immigration is litigation.
Not content creation.
As of 2026:
But:
The enforcement pathway is already legally grounded.
Policy formalization is likely to follow patterns of abuse.
If AI is used at all, the filing must:
Authenticity is protective.
Uniformity is dangerous.
If ICE or a DHS trial attorney argues that your asylum declaration “substantially matches” other filings, your case does not automatically fail.
But it becomes a credibility defense case.
Under Matter of R-K-K-, 26 I&N Dec. 658 (BIA 2015), the Board of Immigration Appeals established that immigration judges may consider significant similarities between statements in different proceedings when making credibility determinations.
However, the BIA also imposed procedural safeguards:
The applicant must receive notice of the alleged similarities.
The applicant must have an opportunity to explain.
The judge must evaluate the totality of circumstances.
This framework is critical.
Similarity is not automatic fraud.
But it can shift the dynamics of the case.
When similarity is alleged, experienced counsel must require the government to identify:
The exact passages claimed to be similar
The comparison documents
The degree of overlap
Whether the similarity is structural, linguistic, or factual
General statements such as “this looks templated” are not enough.
The government must articulate specific comparisons.
Many asylum applicants from the same region may experience:
Similar police tactics
Similar militia threats
Similar detention conditions
Similar political repression
Country conditions reports from the U.S. Department of State frequently document widespread patterns of harm.
The legal distinction is this:
Shared persecution patterns are legitimate.
Identical language patterns raise suspicion.
The defense strategy is to highlight:
Unique dates
Unique emotional reactions
Unique geographic details
Unique corroborating documents
Individualization defeats templating allegations.
Once similarity is raised, corroboration becomes decisive.
That includes:
Medical records
Arrest documentation
Police reports
Witness affidavits
News articles
Psychological evaluations
Expert testimony
When independent evidence aligns with the narrative, similarity arguments weaken significantly.
If a similarity argument is introduced, the applicant must be able to:
Explain how the declaration was prepared
Describe events in their own words
Provide consistent oral testimony
Demonstrate independent knowledge of the facts
Written narrative and in-court testimony must align.
This is where AI-generated over-polishing becomes dangerous.
A declaration must sound like the applicant — not like a law review article.
Credibility findings are reviewed under a highly deferential standard on appeal.
If an immigration judge makes an adverse credibility finding supported by articulated similarities, overturning that decision is extremely difficult.
That is why similarity defense must be proactive — not reactive.
At Herman Legal Group, we treat every declaration as a litigation document from day one.
We are in Phase One of AI use in immigration.
Phase Two will likely involve formal regulatory response.
Based on current trends, several developments are plausible.
USCIS could introduce a certification requiring applicants or attorneys to disclose whether generative AI was used in drafting narrative submissions.
Such certifications could mirror existing perjury language and impose additional verification obligations.
To reduce narrative uniformity risk, USCIS may move toward:
Standardized declaration templates
Guided digital intake systems
Structured text-entry fields
Reducing free-form narrative length reduces similarity analysis complexity.
Public reporting has described systems such as Asylum Text Analytics (ATA), designed to flag duplicate language patterns.
Given existing infrastructure, agencies could:
Expand automated similarity scoring
Flag high-overlap narratives
Trigger Fraud Detection and National Security review
Integrate similarity flags into case management systems
No formal policy has announced this expansion.
But the technological capability exists.
Professional responsibility standards are evolving.
The American Bar Association has already emphasized that lawyers must understand and supervise AI use.
Future EOIR or bar-level rules could require:
Affirmation of AI review
Certification of independent verification
Documentation of human authorship
Immigration law will not remain outside AI governance indefinitely.
Silence from USCIS today does not mean tolerance tomorrow.
The regulatory gap is temporary.
Practices adopted now should assume future scrutiny.
The risk of templated asylum narratives is not new.
Long before generative AI, the immigration system encountered fraud rings involving:
Notarios
Unlicensed preparers
Boilerplate persecution templates
Mass-produced declarations
These schemes often involved identical stories submitted by multiple applicants.
Immigration judges became familiar with:
Repeated metaphors
Identical narrative arcs
Copy-and-paste political persecution claims
Those cases resulted in:
Denials
Fraud findings
Referral for criminal investigation
Permanent immigration consequences
Generative AI introduces a modern parallel.
Instead of human-run template mills, we now have automated narrative generation capable of producing highly similar outputs at scale.
The technology is different.
The pattern risk is not.
When adjudicators encounter similarity, they do not ask:
“Was this written by AI?”
They ask:
“Does this resemble prior templated filings?”
Immigration history shows that mass-produced narratives trigger skepticism.
AI makes mass production easier.
Which means individualized drafting is more important than ever.
Yes, you may use AI tools like ChatGPT for brainstorming or drafting structure. However, you are legally responsible for everything submitted to the U.S. Citizenship and Immigration Services (USCIS).
If AI generates:
Incorrect facts
Inflated achievements
Fabricated legal citations
Misstated immigration standards
You — not the software — bear the consequences.
Every statement in a green card application is submitted under penalty of perjury. AI assistance does not excuse errors.
No federal statute prohibits using AI to help draft immigration materials.
However, submitting false or misleading information can trigger inadmissibility under INA § 212(a)(6)(C)(i) for misrepresentation.
The legal issue is not AI use.
The legal issue is accuracy, truthfulness, and credibility.
There is no publicly announced USCIS policy requiring AI detection or disclosure.
However:
Officers are trained to identify boilerplate language.
Narrative uniformity across filings is noticeable.
Inconsistencies between written submissions and interviews are scrutinized.
Fraud detection infrastructure exists.
Detectability does not require an “AI detector.”
It requires experienced adjudicators recognizing patterns.
Yes.
Under Matter of R-K-K-, 26 I&N Dec. 658 (BIA 2015), the Board of Immigration Appeals held that immigration judges may consider significant similarities between statements submitted in different cases.
Attorneys from U.S. Immigration and Customs Enforcement (ICE) have raised arguments that certain asylum declarations:
Substantially mirror other filings
Contain formulaic language
Appear templated
Similarity alone does not prove fraud. But it can affect credibility determinations.
“Inter-proceeding similarity” refers to substantial linguistic overlap between asylum declarations submitted by different applicants in separate cases.
Under Matter of R-K-K-, judges may consider:
Identical phrasing
Structural replication
Shared narrative sequencing
Repeated metaphors
If similarities are significant, applicants must be given an opportunity to explain them.
Public reporting has described a USCIS system known as “Asylum Text Analytics” designed to flag duplicate language in asylum filings.
Additionally, immigration litigation offices operate enterprise-level document review systems capable of large-scale text search and comparison.
No public rule states that plagiarism software is routinely applied to every case. However, text comparison at scale is technologically feasible within federal systems.
Yes — if it produces:
Generic persecution language
Overly polished academic prose inconsistent with your background
Repetitive structural formatting seen in other cases
Fabricated country condition statistics
Asylum cases depend heavily on credibility under REAL ID Act standards.
If your written declaration does not align with your testimony, credibility may be damaged.
AI can outline hardship categories. It cannot:
Integrate medical documentation accurately
Assess psychological nuance
Align tax records with financial hardship claims
Evaluate country-specific healthcare limitations
USCIS frequently issues RFEs for hardship letters that lack individualized detail. Boilerplate emotional language can weaken discretionary review.
Extreme caution is required.
AI has been known to:
Inflate citation counts
Fabricate journal impact factors
Misstate government program alignment
Overstate leadership roles
NIW petitions are evidence-driven and evaluated under Matter of Dhanasar standards. Any factual inflation may undermine credibility and eligibility.
Shared country conditions can produce similar experiences.
The issue arises when language itself is substantially identical across cases.
Judges distinguish between:
Similar events (which may be legitimate), and
Identical phrasing or structure (which may raise authorship concerns).
Similarity must be evaluated in context.
Under Matter of R-K-K-, you must be:
Notified of the similarities.
Given an opportunity to explain.
Evaluated under the totality of circumstances.
If credibility is questioned, the burden effectively increases. Corroborating evidence becomes more important.
There is no published EOIR policy requiring AI detection software use.
However, judges and government attorneys can:
Compare filings manually
Use document review tools
Analyze structural overlap
Introduce other declarations for comparison
Pattern recognition does not require advanced AI tools.
Yes.
If AI fabricates:
Federal court decisions
Board of Immigration Appeals precedents
Statistical data
Government program references
Submitting those inaccuracies can undermine the filing and potentially trigger fraud concerns.
All citations must be independently verified.
Using AI does not automatically violate ethics rules.
However, attorneys must comply with:
ABA Model Rule 1.1 (Competence)
Rule 5.3 (Supervision of nonlawyer assistance)
Lawyers must verify AI output, protect confidentiality, and ensure accuracy.
Blind reliance on AI-generated content may expose both attorney and client to harm.
There is currently no mandatory disclosure requirement.
However, whether disclosed or not, the content must be accurate, individualized, and defensible under scrutiny.
The focus should not be disclosure alone.
The focus should be reliability and authenticity.
If AI is used at all:
Use it only for structural brainstorming.
Rewrite the content entirely in your own voice.
Verify every fact independently.
Remove generic or templated phrasing.
Ensure alignment with documentary evidence.
Have an experienced immigration attorney review the final version.
AI is a drafting assistant — not a legal strategist.
The biggest risk is credibility damage.
Immigration law is discretionary and adversarial.
If your narrative appears templated, inflated, or inconsistent, it can:
Trigger RFEs
Invite cross-examination
Damage credibility findings
Undermine discretionary relief
Complicate appellate review
In immigration law, credibility is currency.
Uniformity is risk.
AI is not prohibited in immigration filings.
But the legal system already permits scrutiny of patterned narratives. Text comparison tools exist. Litigation doctrine allows similarity arguments.
Before using AI in:
Asylum
Waivers
NIW petitions
VAWA affidavits
Cancellation of removal
You should understand the risk landscape.
At Herman Legal Group, we combine more than three decades of immigration litigation experience with a modern understanding of AI compliance risk.
Because in 2026, technology without legal strategy is exposure.
AI is not illegal.
But immigration is unforgiving.
We are entering an era where:
If your declaration reads like twenty others, you are exposed.
If your narrative reflects individualized truth, supported by evidence and structured for adversarial scrutiny, you are protected.
At Herman Legal Group, we understand both immigration law and AI risk.
In 2026, that dual awareness is not optional.
It is essential.
This directory provides authoritative legal sources and government materials related to AI-generated immigration filings, similarity challenges, asylum credibility doctrine, and technology-driven enforcement.
Matter of R-K-K-, 26 I&N Dec. 658 (BIA 2015)
Board of Immigration Appeals
Authorizes immigration judges to consider significant similarities between statements in different proceedings when evaluating credibility.
https://www.justice.gov/eoir/file/768196/dl
Matter of Dhanasar, 26 I&N Dec. 884 (BIA 2016)
National Interest Waiver (NIW) framework decision.
https://www.justice.gov/eoir/page/file/920996/download
REAL ID Act – Credibility Standard
8 U.S.C. § 1158(b)(1)(B)(iii)
Outlines factors immigration judges may consider in asylum credibility determinations.
https://uscode.house.gov/view.xhtml?req=granuleid:USC-prelim-title8-section1158
U.S. Citizenship and Immigration Services (USCIS)
https://www.uscis.gov
Fraud Detection and National Security Directorate (FDNS)
USCIS fraud detection infrastructure.
https://www.uscis.gov
Executive Office for Immigration Review (EOIR)
Immigration court system under the Department of Justice.
https://www.justice.gov/eoir
U.S. Immigration and Customs Enforcement (ICE)
Office of the Principal Legal Advisor (OPLA) litigates removal cases.
https://www.ice.gov
U.S. Department of Homeland Security – Privacy Impact Assessments
Includes documentation on federal eDiscovery and data analytics systems.
https://www.dhs.gov/privacy-impact-assessments
U.S. Department of State – Country Reports on Human Rights Practices
https://www.state.gov/reports-bureau-of-democracy-human-rights-and-labor/
UNHCR Refworld Database
Country conditions and international protection materials.
https://www.refworld.org
BAJI Report – AI & Immigration Enforcement
Policy research discussing automated systems and text analytics in immigration.
https://baji.org
DHS eDiscovery Privacy Impact Assessment (DHS/ALL/PIA-073)
Discusses enterprise document review and analytics capabilities.
https://www.dhs.gov/publication/privacy-impact-assessment-dhs-all-073-ediscovery
American Bar Association – Model Rules of Professional Conduct
Rule 1.1 (Competence), Rule 5.3 (Supervision), Rule 1.6 (Confidentiality)
https://www.americanbar.org/groups/professional_responsibility/publications/model_rules_of_professional_conduct/
The following Herman Legal Group articles analyze how AI, automation, social media screening, and data analytics intersect with immigration adjudications and enforcement.
U.S. Increases Use of AI in Immigration Enforcement — Efficiency, Risks & Transparency
Analysis of how AI systems and automation are being integrated into immigration enforcement and screening.
https://www.lawfirm4immigrants.com/u-s-increases-use-of-ai-in-immigration-enforcement-efficiency-risks-and-the-battle-for-transparency/
DHS Social Media Rule 2026 — Immigrant Digital Vetting Guide
Explains how DHS and USCIS review social media identifiers, conduct digital vetting, and use automated tools in screening.
https://www.lawfirm4immigrants.com/dhs-social-media-rule-2026-immigrant-digital-vetting-guide/
USCIS Vetting Center, High-Risk Countries & Social Media Screening
Breakdown of how USCIS vetting operations incorporate digital review and screening processes.
https://www.lawfirm4immigrants.com/uscis-vetting-center-high-risk-countries-social-media-screening/
USCIS Oath Ceremony Cancellations & Technology-Driven National Security Holds
Explains how expanded vetting systems and automated review processes can delay or halt naturalization cases.
https://www.lawfirm4immigrants.com/herman-legal-group-uscis-oath-ceremony-cancelled-insights/
Immigration Data Sources 2026 – Free, Public & Trusted Government Data
Comprehensive resource on publicly available immigration data used in case development and research.
https://www.lawfirm4immigrants.com/immigration-data-sources-2026-free-public-trusted/
CLEVELAND (HEADQUARTER)
© Copyright 2025, HLG, LLC. All Rights Reserved
