USCIS itself publicly disclosed that it uses Anthropic Claude, Microsoft Azure and dozens of other AI/ML systems when processing petitions. What is documented, what lawyers suspect, and what nobody knows. May 2026 update.
Information
This is an analysis of the official DHS AI Use Case Inventory (DHS — Department of Homeland Security, the parent organization of USCIS), Privacy Impact Assessments (mandatory public USCIS documents about handling personal data), FOIA litigation texts (Freedom of Information Act — the law by which one can request internal agency documents), and publications of major immigration firms. All quotes include sources.
Contents
- Main news right away
- Anthropic Claude officially operates inside USCIS. Direct DHS quote
- Microsoft Azure translates your foreign documents
- All systems that concern your EB-1A / O-1 / NIW petition
- ATLAS. What it actually does and what it DOES NOT do
- PAiTH Legal Persona. Internal "ChatGPT for USCIS officers"
- What DHS itself says about AI in the inventory. A direct refutation of the panic
- Camp division by evidence strength
- FOIA lawsuits. What plaintiffs want to learn
- Diagnostic silence of insiders and think tanks
- What the community and lawyers say publicly. Direct quotes from Reddit and LinkedIn
- Detailed Reddit analysis. RFE patterns related to AI
- Q3 FY2025 numbers. Hypothesis that AI "ate" the discretionary part
- Specific AI patterns from legal practice
- Context: what is happening with AI across DHS (Mobile Fortify, Hurricane Score)
- What to do right now. Practical conclusions
- What I will monitor in the coming months
- Short summary
If you don’t have time to read the whole article — the essence in seven points
- USCIS officially uses AI/ML. This is not a conspiracy theory. DHS itself publishes the list of its AI systems on the page dhs.gov/ai/use-case-inventory/uscis pursuant to the Advancing American AI Act 2022. As of the January 2026 update of the inventory, about 29 USCIS-related use cases (per counts by law firms SG Legal Group and BMD Law; the American Immigration Council counted 18 purely USCIS applications — the difference lies in what is counted as a use case; some systems are used by USCIS but formally assigned to DHS HQ).
- Anthropic Claude (the same class of AI as ChatGPT) officially operates inside USCIS. When you upload a PDF form through the myUSCIS website (the USCIS online portal for form filing), it is processed by Claude 3.7 Sonnet via Amazon Bedrock. This is a direct DHS quote from use case DHS-2598, status Deployed (in production).
- Key DHS promise: AI does not make final case decisions. That is written in the DHS AI Use Case Inventory itself: each system is labeled as a "decision support tool" or "research tool," with final decisions remaining with a human officer. There are no leaks or documents publicly contradicting this.
- Where AI actually intervenes in your petition: PDF intake via myUSCIS (Anthropic Claude), classification of your evidence (ELIS Evidence Classifier — an internal USCIS ML program that tags your documents), translation of foreign documents (Microsoft Azure), identity linking (PCIS Deduplication — a system that connects all your past records into one profile), fraud screening (ATLAS — a rule-based fraud-checking program), and officer legal research assistance (PAiTH Legal Persona — an internal ChatGPT-like assistant for USCIS officers).
- What ATLAS DOES NOT do. ATLAS does not read the substance of your petition. It does not check whether your Google Scholar citations are genuine, whether publications are real, or whether you meet "extraordinary ability" criteria. Those checks are performed by a human officer.
- AI-pattern RFEs. Major immigration firms (Cozen O'Connor, Reddy Neumann Brown, Greenberg Traurig) since January 2025 have observed RFEs indistinguishable from AI-generation. But these are anecdotal data. The same signs are equally well explained by OCR errors or human templating. Without leaked prompts, it is impossible to distinguish.
- Two FOIA lawsuits (Pangea v. USCIS, No. 1:24-cv-02809; Refugees International v. USCIS, No. 1:24-cv-03559) demand that USCIS disclose records about AI use: AI inventories, training data, policies/guidance, vendor contracts, impact assessments, accuracy/bias testing, notice/redress procedures. Potentially this could include prompts or system instructions if they exist and fall within the scope of the requests. USCIS is producing documents slowly under a rolling production. If courts order full disclosure, we will get the first real internal documents about USCIS AI systems.
Main news right away. This is not a conspiracy theory
When someone on immigration Telegram channels or forums writes “USCIS runs petitions through AI”, the usual reaction is eye-rolling. It sounds like a conspiracy theory. In fact, USCIS itself disclosed this publicly because it is required to by law.
In 2022, the U.S. Congress passed the Advancing American AI Act. Under it, each federal agency must publish a list of its AI systems. DHS does this on a dedicated page: United States Citizenship and Immigration Services – AI Use Cases | Homeland Security. The latest update was January 28, 2026.
As of January 2026, the list included 29 USCIS AI use cases. These are not leaked documents or the result of investigative journalism — this is self-disclosure by USCIS.
Screenshot of the U.S. Department of Homeland Security official page: “United States Citizenship and Immigration Services - AI Use Cases”. Here USCIS itself publicly lists which AI systems it uses. Address: dhs.gov/ai/use-case-inventory/uscis. On the left you can see navigation across DHS components (USCIS, USCG, CBP, CISA, FEMA, ICE, USSS, TSA), indicating that this disclosure is part of the federal AI disclosure system — a legal requirement under the Advancing American AI Act 2022.
Main principle of this analysis
I separate three types of assertions:
- 📋 PROVEN — a direct quote from an official DHS or USCIS document with a source link. USCIS has publicly admitted it.
- ⚠️ LOGICALLY FOLLOWS — my inference that is not a direct quote. USCIS did not say it directly, but it follows from other confirmed facts.
- 🔴 UNPROVEN or REFUTED — rumor or hypothesis without confirmation, or something DHS has directly refuted.
Anthropic Claude officially operates inside USCIS. Direct DHS quote
If I were to write a clickbait headline: “USCIS uses the same AI you use at home.” That would be true. Direct quote from the DHS AI Use Case Inventory, use case DHS-2598 “PDF Intake (PDFI) for myUSCIS”, status Deployed:
“PDF Intake (PDFI) is a new form intake channel that allows applicants and attorneys to upload completed PDF forms online. Scanned PDFs submitted through MyUSCIS must be validated against form-specific business rules related to both the overall document and the contents of specific fields. The service can process a scanned input document and return all information pertinent to these validation rules in a consistent structure (JSON) to a user-facing ELIS microservice. The GenAI powered library utilizes Amazon Bedrock – Anthropic Claude 3.7 Sonnet V1 Foundation Model to extract data from PDF forms.”
Translation: PDF Intake is a new form intake channel allowing applicants and attorneys to upload completed PDF forms online. Scanned PDFs submitted through MyUSCIS must be validated against form-specific business rules. The service can process a scanned input document and return all information pertinent to these validation rules in a consistent structure (JSON) to an ELIS microservice. The GenAI library uses Amazon Bedrock — Anthropic Claude 3.7 Sonnet V1 Foundation Model to extract data from PDF forms.
⚠️ Important about disclosure accuracy
The phrase “Anthropic Claude 3.7 Sonnet V1” is the literal DHS text from the latest annual inventory update (Jan 28, 2026). This is the agency’s self-disclosure, not made up. But even if the DHS inventory names a specific model, it does not reveal runtime configuration, prompts, guardrails, error rates, or exactly which forms actually flow through that pipeline.
What this means in plain language. When you file a form online through myUSCIS, Anthropic Claude processes it. This is the same company that makes Claude.ai (a competitor to OpenAI’s ChatGPT). Claude 3.7 Sonnet is the specific model version. AWS Bedrock is Amazon’s marketplace for language models, which agencies can use to run LLMs (Large Language Models) like ChatGPT or Claude in a secured cloud.
Claude reads your PDF and transforms all data into a structured format (JSON — a key-value data structure computers can read) that USCIS’s internal system ELIS (Electronic Immigration System) understands. After that, the officer sees your form in their normal interface.
⚠️ Logically follows (but DHS does not say this explicitly)
I-140 (for EB-1A and EB-2 NIW) and I-129 (for O-1) have been available for online filing via myUSCIS since 2023. Therefore, when you file online, your PDF likely passes through Claude. But DHS’s DHS-2598 does not name specific forms. That's my inference. If you file by mail to the Lockbox (the postal intake for USCIS paper petition packets), your PDF goes through a different system (Intelligent Document Processing, case DHS-2385), without Claude.
Microsoft Azure translates your foreign documents
If you submit EB-1A or O-1 evidence in Russian, Chinese, Korean, or any other language — the officer may run them through Microsoft Azure’s machine translation. Quote from use case DHS-2305 “USCIS Document Translation Service” (status Pre-deployment — being rolled out, marked as High-Impact — DHS officially acknowledges the system affects people’s rights):
“The USCIS Document Translation Service provides the ability for an immigration officer to upload an evidence document written in another language and request a nearly instantaneous English translation. Within a matter of minutes, the service delivers an image-to-image translation that is displayed side by side with the original in the ELIS Digital Evidence Viewer. The service integrates Global and ELIS services with the Microsoft Azure AI Translator Service. Evidence documents include passports, national identifications, birth certificates, and more complex documents such as police reports.”
Translation: The USCIS Document Translation Service lets an immigration officer upload an evidence document in another language and request an almost instantaneous English translation. Within minutes, the service delivers an image-to-image translation displayed side-by-side with the original in the ELIS Digital Evidence Viewer. The service integrates Global and ELIS services with the Microsoft Azure AI Translator Service.
What Microsoft Azure AI Translator is in plain language. It is Microsoft’s cloud machine-translation service. Similar to Google Translate, but enterprise-grade and running in Azure. Quality is better than consumer Google Translate, but it’s still machine translation.
⚠️ What this means for your petition (my inference)
Machine translation from Azure does not replace certified translation (a human-certified translation with the translator’s signature) under 8 CFR § 103.2(b)(3) — the Code of Federal Regulations section that specifies requirements for documents in immigration cases. Regulation requires a certified human translation with a certificate of accuracy. But if an officer is unhappy with your translation or wants to double-check, they can get a machine translation from Azure with one click. And they will evaluate the meaning of your Russian diploma or award phrasing using that machine translation. Machine translation can distort nuances — especially academic degrees, precise award wording, and technical terms.
All systems that concern your EB-1A / O-1 / NIW petition
Below is an analysis of the main systems from the DHS AI Use Case Inventory that logically relate to talent visa petitions. For each I give a direct DHS quote, a plain explanation, and an explicit separation of “what DHS said” versus “what I infer.”
ELIS Evidence Classifier (DHS-16). The program decides what the officer sees first
When you upload an EB-1A petition with dozens or hundreds of pages of evidence (recommendation letters, diplomas, patents, citation reports from Google Scholar), the officer physically cannot read everything. So USCIS deployed a program that automatically labels each page.
“The Evidence Classifier Service is a machine learning (ML) solution that reduces the time spent by adjudicators and contractors sifting through digital evidence. The solution systematically tags and surfaces critical evidence types for the adjudicators in Electronic Immigration System (ELIS)... When a user opens a case with potentially hundreds of pages of evidence documents they have clickable bookmarks from these tags that will jump directly to the corresponding page.”
Translation: The Evidence Classifier is an ML solution that reduces the time adjudicators and contractors spend reviewing digital evidence. The solution systematically tags and highlights critical evidence types for adjudicators in ELIS... When a user opens a case with hundreds of pages of evidence, they get clickable bookmarks from these tags that jump directly to the corresponding page.
In plain language. The program looks at every page of your PDF and labels it: “this is a passport”, “this is a recommendation letter”, “this is a diploma”, “this is a journal publication.” The officer opens the case and sees a list of labels as bookmarks. Click the label, and you jump to the relevant page.
⚠️ What this means for your petition (my inference)
The program decides what the officer sees first. If the ML mislabels your Nature publication as "other document," the officer might not open it in the context of the "scholarly articles" criterion. Lawyers (Cozen O'Connor, May 2026) suspect that mis-tagging causes RFEs claiming "missing" evidence that was actually submitted. DHS does not publish error rates for this system.
ATLAS. What it actually does and what it DOES NOT do
Important about the name: ATLAS is not an acronym. USCIS never published an expansion. In the official document DHS/USCIS/PIA-084 (Privacy Impact Assessment — mandatory public document on how the system handles personal data, July 2021) the system is simply called “ATLAS” — an internal codename like Apollo, Phoenix, or Liberty.
Title page of the official Privacy Impact Assessment for the ATLAS, DHS Reference No. DHS/USCIS/PIA-084, October 30, 2020 (last updated July 2021). A 31-page technical description of USCIS’s internal automated screening system. This is the primary source for all statements about ATLAS — the original document is hosted on dhs.gov as the formal privacy disclosure.
Now to the substance. ATLAS is not a neural network, not an LLM, not machine learning in the narrow sense. It is a rule-based system (a program that works by strictly coded “if-then” rules, without training on data) that automatically checks each USCIS petition against external databases. It is deployed inside FDNS-DS (Fraud Detection and National Security Data System — USCIS’s internal system for investigating fraud and national-security-related issues).
“ATLAS is used as both an automated check service platform and rule-based screening platform for USCIS... ATLAS rules are designed to identify potential fraud, public safety, and national security concerns. ATLAS applies rules against the biometric and biographic data of USCIS applicants, petitioners, beneficiaries, sponsors, and preparers...”
Translation: ATLAS is used as a platform for automated checks and rule-based screening for USCIS... ATLAS rules are designed to identify potential fraud, public safety, and national security concerns. ATLAS applies rules to biometric and biographic data of applicants, petitioners, beneficiaries, sponsors, and preparers.
What biometric and biographic data are. Biometrics — fingerprints, facial photographs. Biographic — name, date of birth (DOB), addresses, employers, phone numbers, passport numbers, A-Number (Alien Registration Number — your unique USCIS immigration number).
What ATLAS actually checks
- Biometric matches with IDENT (DHS biometric database), HART (Homeland Advanced Recognition Technology — the new IDENT successor), ABIS (Automated Biometric Identification System — DoD system) — to detect prior multiple identities or prior filings under different names;
- Names and DOB against FBI Name Check, TECS (Treasury Enforcement Communications System) — to see if you are on a watchlist or have criminal records;
- Links between people (same petitioner, sponsor, preparer across multiple cases) — to detect a single attorney filing many suspiciously similar cases;
- Addresses, phones, employers — to detect sham companies or known fraud addresses;
- Source country flags — countries with higher fraud risk trigger additional checks.
🔴 What ATLAS DOES NOT do (important to understand)
- It does not read the substance of your petition.
- It does not verify that your Google Scholar citations are real.
- It does not determine whether your journal publications are genuine.
- It does not check whether signatures on recommendation letters are forged.
- It does not assess whether you meet the "extraordinary ability" criteria.
- It does not judge whether your role in projects is exaggerated.
Those checks are done by a human officer. If the officer suspects substantive fraud, they refer the case to FDNS for manual investigation. ATLAS only highlights identity and relational issues.
Is ATLAS applied to I-140 (EB-1A) and I-129 (O-1)?
“the term immigration request includes all benefit requests (as that term is defined in Title 8, C.F.R. Part 1.2).”
Translation: the term "immigration request" includes all benefit requests (as defined in Title 8, C.F.R. Part 1.2), i.e., any benefits application filed with USCIS.
⚠️ My inference (not a quote)
8 CFR § 1.2 defines "benefit request" as any form submitted to USCIS. Legally, that covers I-140 (EB-1A, EB-2 NIW) and I-129 (O-1). However, USCIS in PIA-084 does not specify that these particular forms are run through ATLAS. By the logic of the regulation — yes, they should be. But there is no direct public confirmation that ATLAS explicitly checks EB-1A forms.
PAiTH Legal Persona. Internal “ChatGPT for USCIS officers”
This is the most important system for understanding how AI might end up in the wording of your RFE. Direct quote from DHS:
“PAiTH (Private AI Tech Hub) will serve as USCIS's internal AI workforce assistant... The system will provide role-specific AI assistance to USCIS staff across six functional areas: contracts/acquisition, legal research, language translation, software development, security compliance, and financial operations... Legal Persona: Legal research summaries, statute and regulation citations (INA, CFR), case law analysis, draft legal memoranda outlines, document summaries with legal issue identification... accompanying policy will require human review before being used in any official decision-making, formal communications, and/or reporting.”
Translation: PAiTH will serve as an internal AI assistant for USCIS staff... The system will provide role-specific AI help in six functional areas. Legal Persona: legal research summaries, statute and regulation citations (INA, CFR), case law analysis, draft outlines of legal memoranda, document summaries with identification of legal issues... policy accompanying it will require human review before any official use.
In plain language. A USCIS adjudicator who handles your EB-1A might open a chat interface in PAiTH (Private AI Tech Hub — USCIS’s internal AI hub) and ask, for example, “what case law exists on original contributions of major significance?” or “summarize the recommendation letters in this petition.” The AI will provide:
- Legal research summaries — overviews of legal materials
- Statute and regulation citations — INA and CFR references
- Case law analysis — analysis of court decisions
- Draft legal memoranda outlines — internal memo drafts
- Document summaries — summaries of your documents
⚠️ What this means for the text of your RFE (my inference)
PAiTH is the most direct AI→RFE channel. If AI supplies the officer with a case-law analysis that misidentifies a controlling precedent, or if it mis-summarizes a recommendation letter, that error can make it into the final RFE. USCIS explicitly requires "human review before being used in any official decision-making" — meaning the officer must check AI output. But how strictly this requirement is followed under workload pressures is publicly unknown.
This explains systematic patterns of mis-cited case law documented by The Seltzer Firm (cases like Silverman v. Eastrich, APWU v. Potter, Visinscaia v. Beers appearing in EB-1/O-1 RFEs despite being unrelated to immigration). Maybe these are officer templates. Maybe PAiTH hallucinated. Distinguishing is currently impossible.
What DHS itself says about AI in the inventory. A direct refutation of the panic
When people panic and claim “USCIS now lets AI decide everything”, there is a primary source that directly contradicts this: the DHS AI Use Case Inventory. For example, in the description of Text Analytics Data Science Sentence Similarity Model (DHS-130, aka ATA), DHS writes directly:
“Text Analytics does not make any determinations or decisions but is instead utilized as a research tool by staff in the course of their duties.”
Translation: Text Analytics does not make determinations or decisions; it is used by staff as a research tool in the course of their duties.
This formula repeats across descriptions of each USCIS AI system in the inventory: “decision support tool”, “research tool”, “human review required.” The American Immigration Council analysis “Invisible Gatekeepers” confirms the official DHS position: “humans make decisions about detention, deportation, and eligibility, with AI tools playing a supporting role only.”
This is DHS’s public promise. AI is defined as:
- Decision-support (helping officers make decisions)
- Training augmentation (helping train officers)
Not as autonomous adjudication.
If a leak later shows otherwise, that will be a scandal and grounds for litigation. For now, no such leak exists. This is the strongest official counter-argument to the claim “AI decides my case.”
Camp division by evidence strength
Unlike the AOS memorandum topic, on AI at USCIS there isn’t a simple pro-vs-anti split. Everyone agrees AI is used. The debate is about evidentiary strength: what is a fact, what are circumstantial signs, what is rumor. Therefore I group claims not by opinion camps but by source-level.
Level 1. PROVEN — direct DHS quotes
Everything analyzed above: ELIS Evidence Classifier, PDF Intake via Claude, Document Translation via Azure, ATLAS, PCIS, PAiTH, DHSChat. Source — DHS AI Use Case Inventory + PIA documents.
Also:
- Eric Hysen, CIO DHS, Dec 16, 2024: “158 active use cases, compared to 67 total use cases in 2023. We identified 39 safety- and/or rights-impacting use cases.” [DHS source].
- DHS OIG-25-10 audit (Office of Inspector General — January 2025): the internal auditor said oversight over AI is insufficient.
- USCIS–Palantir VOWS contract (Palantir, Vetting Of Wedding-based Schemes), Oct 2025, <$100K — [Fortune], Dec 9, 2025. DOES NOT concern EB-1/O-1; this is about marriage fraud.
Level 2. LOGICALLY FOLLOWS — inferences from confirmed facts
Here analysis begins. USCIS does not say “ATLAS checks EB-1A” explicitly. But 8 CFR § 1.2’s definition of “benefit request” covers all forms, so logically ATLAS should check EB-1A. Same logic applies to ELIS Evidence Classifier (USCIS doesn’t state it tags EB-1A recommendation letters, but logically — yes), and PCIS (Person-Centric Identity Service — the system that aggregates your past records into one profile) — mismatches between DS-160 (nonimmigrant visa form) and I-129 could trigger flags, but DHS does not explicitly assert this.
Level 3. LAWYERS’ SUSPICIONS — anecdotal data
Cozen O’Connor (Scott Bettridge, David S. Adams), May 2026
“USCIS has not published any error-rate data, and practitioners report RFEs for documents that were in fact submitted, consistent with classifier mis-tagging.”
Translation: USCIS does not publish any error-rate data, and practitioners report RFEs for documents that were in fact submitted, consistent with classifier mis-tagging. Source.
Cozen O’Connor — a major U.S. law firm representing corporate clients — lists four specific patterns. I will analyze each: what happens, how it looks when you get an RFE, how to defend.
Title of Cozen O'Connor publication, Apr 27, 2026: “Growing Use of Artificial Intelligence in U.S. Immigration Adjudications Is Driving Higher RFE and Denial Rates.” Authors: Scott Bettridge (Chair, Immigration Practice) and David S. Adams (Member). This is a formal client alert from partners at one of the largest U.S. law firms. Source.
Pattern 1. Mis-tagged evidence. RFE for documents you definitely submitted
When you upload a petition through myUSCIS, the ELIS Evidence Classifier automatically scans each page and labels it: “Recommendation Letter,” “Passport,” “Diploma,” “Journal Publication.” The officer opens your file and sees a ready list of label-bookmarks. The officer works off those bookmarks.
The problem is the ML classifier sometimes mislabels. Say you included 8 recommendation letters. Six are properly tagged as “Recommendation Letter.” Two are mistakenly classified as “Other Document” — maybe one has unusual formatting, another is a scan with shadows. The officer opens the “Recommendation Letters” bookmarks, sees 6 instead of 8, and writes in the RFE: “petitioner submitted only 6 recommendation letters.” You open your copy — you see all 8 present. Strange situation: you are right, and the officer is also right by his view — he truly saw only 6 because the classifier didn’t show the others.
To reduce this risk: give files clear names indicating document type (Recommendation_Letter_Prof_Smith_Stanford.pdf instead of bland RecLetter1.pdf), do not combine multiple different evidence items into one huge 200-page PDF, include a cover letter with a clear table of contents: “Exhibit A-1 — Letter from X — pages 47-50.” If an RFE claims documents are missing, respond by explicitly pointing to where those documents are in the original petition, with Exhibit references, page and paragraph.
Pattern 2. Cross-document mismatch. When one inconsistent line triggers an RFE
USCIS has a Verification Match Model. It compares data across all your documents and flags inconsistencies. If your DS-160 (nonimmigrant application) lists “Senior Software Engineer,” an I-129 lists “Principal Software Engineer,” and a recommendation letter says “Lead Engineer,” the program sees three different titles and sets a flag. To a human, these are clearly the same role with slight variations. To an automated system, those are three separate strings.
Concrete SG Legal Group example: their client received an RFE for “date inconsistencies.” They investigated and found that one Russian document had the date in GOST format (15.03.2023), the English translation used U.S. format (03/15/2023), and a third document used European format (15/03/2023). It’s the same date written three ways. AI saw three different strings and flagged a contradiction. The lawyer noted no other logical grounds for the flag — so it appears the machine raised it, not a human.
For Russian applicants there are typical triggers. Name transliteration inconsistencies (Egor / Yegor / Igor / Е. Akimov across documents) — include a name variation memorandum listing all variants and explaining which standard is used where (GOST R 7.0.34-2014 in Russian documents, BGN/PCGN for U.S. passport transliteration). Use a single official English name for Russian institutions (Skoltech, Skolkovo Institute of Science and Technology, Skolkovo Tech). Keep job titles consistent across documents. Dates on Soviet-era diplomas often appear as 15.06.1990 — in an Apostille translation they may become 06/15/1990 or 15 June 1990, which USCIS might treat as different. Ensure the notarized translation states which format is used.
Pattern 3. Ghost text. What you don’t see, AI does
Modern PDFs can contain an invisible text layer. Visually you see the text clearly, but a program reading the PDF machine-wise (for example, USCIS’s AI) reads both visible and hidden layers. Sources of hidden layers:
- You scanned and OCR’d the document — an invisible OCR layer sits over the image;
- You converted Word to PDF — hidden comments, tracked changes, metadata may remain;
- You pasted text from ChatGPT or Claude into Word — invisible service tags can remain;
- You used an internet template and edited it — old template text may remain invisibly in the file.
In practice this looks like: you wrote a recommendation letter from scratch and exported to PDF. Visually the text is correct. USCIS AI reads visible and hidden text. If the hidden layer still contains a phrase from an old template like “Dear Hiring Manager,” the AI may flag your letter as “boilerplate detected.” Worst case: if the hidden layer contains phrases typical of AI-generation (AI-style turns of phrase), USCIS AI might mark your letter as AI-generated — even if you wrote it yourself.
You can check for ghost text easily. Open the PDF in Adobe Acrobat or Preview on Mac and press Ctrl+A / Cmd+A (select all). If more text is selected than you see, there is a hidden layer. Alternatively, use command-line: pdftotext your_file.pdf - to output all text. If ghost text is found and substantial, the safest fix is to print the critical document, re-scan it, and save as a clean PDF. That removes hidden layers.
Pattern 4. AI-generated boilerplate RFEs. A template generated a template
If the prior three patterns are about AI reading your documents, this one is about AI writing officers’ response documents. USCIS’s internal AI assistant (PAiTH Legal Persona or similar) can generate a draft RFE for the officer. The officer quickly reviews, edits one or two phrases, and sends. Under heavy workload, this mode can become widespread.
Reddy Neumann Brown precisely describes such RFEs: “they look official but read as if no human actually reviewed the file.” They list tell-tale signs.
Identical phrasing repeated for every criterion — e.g., “This criterion has not been met because…” repeated ten times with no variation. Citation of case law irrelevant to immigration — e.g., Silverman v. Eastrich (a credit-default case) or APWU v. Potter (an anthrax-in-mail case) appearing in EB-1A RFEs as support for a high threshold. Long paragraphs copied verbatim from the USCIS Policy Manual without any analysis of the specifics of your petition. Mentioning an employer or field unrelated to you — a Reddit case where an RFE mentioned “FAANG” though the applicant never worked for those companies. Phrases characteristic of language models — “opine”, “juxtapose”, “comprehensive examination”, “robust analysis” — lawyer Ksenia Maiorova calls these “AI tells.”
If you receive such an RFE, respond as follows: for each assertion “evidence missing” provide the exact reference to the original petition — Exhibit, page, paragraph. That forces the officer to acknowledge the evidence was submitted. If an RFE cites case law unrelated to immigration, point that out and request the relevant precedent. If the RFE contains factual errors (wrong employer, swapped names, incorrect country), document them as factual errors requiring USCIS explanation. Preserve the full RFE text and consider anonymously posting it to the community (Trackitt, AILA forums, relevant subreddits) to build a corpus supporting future litigation.
Numbers that show scale
Cozen O’Connor reports: denial rates for EB-2 NIW increased to roughly 40%, and EB-1A is under heightened scrutiny.
For context: historically EB-2 NIW approvals were around 70–75% (denials 25–30%). A rise to 40% denials means almost half the prior improvement reversed in a year. EB-1A historically approved 75–80%; in Q3 FY2025 approval dropped to 66.6% — the lowest in 3 years, meaning denials rose from ~20% to ~33%. O-1 remains high at 93.8% approval with little change.
This contrast suggests a pattern. EB-1A and NIW require discretionary assessment — an officer qualitatively judges whether the applicant meets “extraordinary ability” or “national importance.” AI struggles with qualitative judgments. O-1 is more checklist-oriented (did you receive specific awards, publish a certain number of articles, hold specified roles?). AI handles checklists better. The drop in approvals specifically in discretionary categories while checklist categories hold steady is a characteristic signature that AI is affecting the discretionary part and causing errors there.
Reddy Neumann Brown PC, January 2026
“disorganized, boilerplate recitations of USCIS Policy Manual provisions, field adjudicator guidance, or regulatory language, often copied verbatim and presented without analysis, explanation, or reference to the specific evidence already submitted in the petition... looks official but reads as though no human being meaningfully reviewed the filing”
Translation: disorganized boilerplate recitals of USCIS Policy Manual provisions, copied verbatim and presented without analysis... looks official but reads as though no human being meaningfully reviewed the petition. Source.
The firm explicitly compares this to AI hallucination and refused to recommend premium processing for EB-1A and NIW (premium processing — expedited service for $2,805 guaranteeing a response in 15–45 days). Premium became a “fast track to an RFE” rather than to approval.
The Seltzer Firm. Documented mis-citation of case law in EB-1/O-1 RFEs
Specific cases are systematically mis-cited:
- Silverman v. Eastrich Multiple Investor Fund — actually a credit-default case, unrelated to immigration, yet cited regarding "original contributions of major significance";
- APWU v. Potter — actually about an anthrax investigation in postal facilities;
- Visinscaia v. Beers — a case about a Moldovan ballerina, mischaracterized;
- Matter of Caron International — selectively quoted without limiting context.
[Source: The Seltzer Firm].
BMD Law, “Invisible Algorithms” — an honest caveat
“Some practitioners have reported receiving RFEs that contain language or structure consistent with AI-assisted drafting. This has not been confirmed by USCIS, and there is currently no public evidence that AI is being used to generate adjudication decisions or official correspondence.”
Translation: Some practitioners reported RFEs with language/structure consistent with AI-assisted drafting. This is not confirmed by USCIS, and currently there is no public evidence that AI is used to generate adjudication decisions or official correspondence. Source.
Main caveat at this level
The same signs lawyers attribute to "AI patterns" can equally be explained by:
- OCR errors (there is academic work on improving NLP accuracy over OCR documents);
- Longstanding agency templates that USCIS has used for decades;
- Human copy-paste practices;
- Actual AI-assisted drafting.
Without leaked prompts (the text instructions used to feed AI), screenshots, or FOIA releases, we cannot distinguish between "AI wrote the RFE" and "an officer copied from a template and made mistakes."
Are there any leaked USCIS prompts? As of May 2026 — no. I checked multiple channels:
- No results in open search for "USCIS leaked prompt" / "ELIS screenshot" / "USCIS officer interface";
- 404 Media (known for leaks around Mobile Fortify) — no USCIS prompt publications;
- WikiLeaks / DDoSecrets — no USCIS-relevant prompt documents;
- FOIA releases in Pangea v. USCIS and Refugees International v. USCIS — ~1,677 pages released but without prompts or system instructions;
- USCIS GitHub repositories — empty.
So we know AI is used (DHS confirmed), and we know some vendors and infrastructure (Anthropic via AWS Bedrock). But the specific instructions given to Claude or other systems have not been publicly revealed.
If the situation changes, there are two likely paths: (1) a court orders USCIS via FOIA litigation to produce more specific records about AI use (potentially including prompts if they exist as independent documents), or (2) an internal leak via 404 Media or similar. Mobile Fortify was revealed by leaked emails in June 2025 six months before official acknowledgment.
Level 4. REFUTED or NOT CONFIRMED
- "AI makes final decisions on EB-1/O-1" → directly refuted in DHS AI Use Case Inventory: each system is labeled a "decision support tool" or "research tool";
- "Leaked USCIS prompts" → not publicly available;
- "Screenshots of internal adjudicator interfaces" → none published;
- "USCIS partnership with OpenAI" → not confirmed. With Anthropic — yes, via AWS Bedrock for PDF Intake;
- "Matter Helper" internal USCIS AI → rumor, likely confusion with the commercial tool "Matter" used by lawyers;
- "Babel Street processes USCIS petitions" → Amnesty International (July 2025) did not confirm a USCIS–Babel contract. Babel Street is used by State Dept / CBP, but not USCIS;
- r/USCIS_Officers — such a subreddit does not exist.
FOIA lawsuits. What plaintiffs want to learn and what the next year will show us
Two FOIA suits could change the picture in the coming months. Both were filed in D.D.C. (U.S. District Court for the District of Columbia).
Pangea Legal Services v. USCIS, No. 1:24-cv-02809-ACR (D.D.C., Oct 3, 2024)
Plaintiffs: Pangea Legal Services, Mijente Support Committee, Just Futures Law (the latter acts both as plaintiff and counsel). All details below come directly from PACER as of May 25, 2026.
Screenshot of the real docket from CM/ECF. Shows: filing date 03/10/2024, COMPLAINT against DHS/ICE/USCIS, filed by Pangea Legal Services + Just Futures Law + Mijente Support Committee, filing fee $405, case assigned to Judge Ana C. Reyes 07/10/2024. Counsel Sejal Zota (Just Futures Law). All 22 attachments (16 exhibits + civil cover sheet + 5 summonses) attached to the complaint. PACER is paywalled — this analysis uses paid primary sources rather than recaps.
Judge: Ana C. Reyes — Biden appointee, sworn in Feb 2023, formerly a partner at Williams & Connolly. Known for firm positions on transparency and FOIA.
Critical detail: Judge Reyes is handling both key FOIA suits against USCIS about AI — Pangea and Refugees International. PACER marked them as Related Cases. Not coincidental: D.D.C. often groups similar cases with one judge. Her decision in one will likely affect the other.
Plaintiffs’ counsel (as of May 2026):
- Sejal Zota (Just Futures Law) — active since Oct 2024
- Yihong Mao (Just Futures Law) — joined Jan 2026
- Dinesh McCoy (Asian American Legal Defense Fund) — left Aug 27, 2025
- Daniel Werner (Just Futures Law) — left Dec 17, 2025
Defendant’s counsel: Esther You (DOJ-USAO) — now lead, replaced Kartik Venguswamy (ArentFox Schiff). DOJ took over the case — a signal the federal government treats the matter as high priority.
The suit requests 16 categories of records for each key AI system:
- Lists of AI tools;
- Training data for models;
- Policies and training materials for officers;
- Vendor contracts;
- Records for "Pangea Text" (internal name for Asylum Text Analytics);
- AI for behavior/emotion/social media analysis;
- Sharing AI-generated information with other agencies;
- PIA and Algorithmic Impact Assessments (AIA);
- Waivers from AI Act requirements;
- Consultations with affected communities;
- Redress procedures (how to contest AI determinations);
- Bias monitoring and testing;
- Audits, validation, accuracy metrics;
- Notice policies (do they notify applicants about AI use);
- Opt-out policies (can you opt out of AI processing);
- Records concerning "rights-impacting" determinations.
Real production numbers as of March 31, 2026 (from Joint Status Report Doc #29 signed by Esther You for DOJ and Yihong Mao for Just Futures Law):
- USCIS: production was completed on June 30, 2025. The JSR does not specify exact page counts. Plaintiffs are reviewing documents and both sides "intend to work together in good faith to address any remaining issues."
- ICE: production completed April 24, 2025. Plaintiffs challenged adequacy of search on Sept 22 and Oct 7, 2025. On Feb 13, 2026 ICE produced 7 more pages; plaintiffs still contest.
- DHS: identified ~3,500 pages for narrowed requests. As of March 31, 2026 it released only: 3 pages (Dec 30, 2025) + 127 pages (Jan 28, 2026) + 29 pages (Feb 13, 2026) = ~159 of 3,500. The rest went for consultation with Other Governmental Agencies on Jan 30, 2026. DHS failed to meet the promised 500 pages/month pace.
Government shutdown since Feb 14, 2026. From the JSR: “DHS, which includes ICE, is currently experiencing a lapse in appropriations which began February 14, 2026. DHS FOIA employees are furloughed and have not been excepted to work on FOIA litigation.” So DHS FOIA staff are furloughed and production is frozen. Plaintiffs explicitly reserved the legal question whether DHS can release FOIA productions during a lapse in appropriations.
Defendant counsel — United States Attorney Jeanine Ferris Pirro. Her involvement is a political signal: Pirro is a former Fox News host appointed U.S. Attorney for D.D.C. under the Trump administration in 2025. DOJ’s signature on the latest JSR shows the case is being handled in-house.
Next checkpoint: May 29, 2026 — next Joint Status Report. That is four days after this article’s date and will indicate whether the shutdown ended and production resumed, or whether plaintiffs will move to compel.
What is NOT in the docket as of May 2026: Motion for Summary Judgment, Vaughn Index, Judge Reyes’s ruling on the merits. Despite 18 months of production and 29 docket entries, there is no judicial ruling that would have disclosed USCIS prompts. Expect leaked prompts via Pangea not before 2027 realistically — more likely 2028.
Documents directly from plaintiffs:
- Just Futures Law — DHS AI FOIA filings page — case page with key filings and updates
- Just Futures Law — "Reining in AI Surveillance" — a continuously updated section about DHS AI tools
- Automating Deportation: The Artificial Intelligence Behind The Department of Homeland Security's Immigration Enforcement Regime (PDF, June 2024) — a primary detailed report by Just Futures Law + Mijente that underlies the Pangea suit. If you read only one document on AI at DHS, read this. It explicitly names commercial AI tools DHS procured (e.g., LexisNexis Accurint — available to 11,000+ ICE agents for vetting/screening/targeting).
How FOIA litigation works (briefly)
FOIA suits are not criminal or standard civil suits. Procedure:
- File a FOIA request. Any person or organization can request internal agency documents. Agency must respond within 20 business days.
- If agency denies or delays — plaintiff may sue in federal court to compel production (this is what Pangea/Mijente did in Oct 2024).
- Production — the time-consuming part. The agency searches for responsive records, decides what to produce, what to redact, what to withhold, and usually sends documents in batches (rolling production).
- Joint Status Report — both parties report to the court every 60–90 days on production progress. If dispute persists, plaintiffs file a motion to compel; defendants may move for summary judgment.
- Vaughn Index — a formal list of documents the agency withheld, with legal justification. It is critical in FOIA litigation to challenge withholdings. If a Vaughn Index has not been produced, the case is far from resolution.
- Court ruling — judge decides what must be released. FOIA litigation can take years.
In Pangea: 18 months passed, eight JSRs, thousands of pages produced — but the most sensitive documents remain in consultation or withheld. No Vaughn Index yet. Then the shutdown happened.
What this means for EB-1A / O-1 / NIW filers
Practical takeaways from Pangea’s status as of May 2026:
- Do not expect fast disclosure of USCIS prompts via court. Earlier estimates that Pangea and Refugees International could reveal prompts within 12–24 months were optimistic. Realistic timeline: ruling on merits not before 2027; plausible disclosure of materials revealing how AI really works — 2028–2029. Filing a petition in 2026 proceeds without knowledge of AI internal details.
- USCIS already produced records to plaintiffs, but we don’t see them yet. USCIS’s production completed June 30, 2025. Just Futures Law is reviewing what it received. If they publish significant findings on justfutureslaw.org/aitech, monitor their site or subscribe for updates.
- Government shutdown is a new risk to all immigration cases. DHS FOIA staff furloughed since Feb 14, 2026. But the shutdown affects more than FOIA — regular adjudicators also work with reduced staff. This helps explain why RFEs in early 2026 may be even more boilerplate: officers are overburdened; templates (or AI assistance) are the only means to continue processing.
- Plaintiffs’ activity is intensifying. Their challenges to adequacy of search show they are pushing beyond polite negotiation to real pressure. If plaintiffs file a motion to compel after May 29, 2026, expect more aggressive litigation.
- Alternative route — individual FOIA requests. Pangea asked for system-wide records. Any EB-1A/O-1/NIW petitioner can file a personal FOIA request for their A-file (USCIS case file) requesting "including all AI-generated summaries, classifications, flags, and alerts in my case." USCIS typically releases A-files in 30–90 days. This yields your case-specific documents faster than Pangea’s systemic litigation.
- Prepare petitions for the 2026–2028 horizon without expecting disclosure. The practical recommendations in the next section work regardless of whether USCIS prompts are disclosed. They are basic protections: clear file names, clean OCR layer, certified translations, name-variation memorandum, consistency across documents.
- If you get a denial in 2026 — consider Mukherji v. Miller. The Mukherji decision (D. Neb., Jan 28, 2026) found certain USCIS final-merits determinations arbitrary and capricious under the APA (Administrative Procedure Act) after Loper Bright. Mukherji provides a legal basis to challenge nontransparent AI-driven decisions without waiting for Pangea to reveal prompts.
Refugees International v. USCIS, No. 1:24-cv-03559 (D.D.C., Dec 20, 2024, Judge Ana C. Reyes)
Counsel: Harvard Immigration & Refugee Clinical Program (Sabrineh Ardalan, Jessenia Class, Martha Ball) + Jenner & Block LLP.
The suit seeks:
- Internal guidance on ATA (Asylum Text Analytics) use in asylum adjudication;
- Training materials, including “lines of questioning and RFEs based on ATA findings”;
- A sample redacted Pangea Text report (ATA output), Statement of Findings, RFE;
- 20 categories of statistics: how many cases screened, flagged by nationality, by office, denials, deportations.
⚠️ Important detail
ATA in the DHS AI Inventory was listed → delisted → relisted → moved to "inactive" within 11 days in December 2024 (Dec 9–20). This shows DHS manipulates its own inventory classifications in response to pressure.
Letter from 142 organizations to Mayorkas, Sep 4, 2024
A coalition of 142 organizations (confirmed by Just Futures Law), including EFF (Electronic Frontier Foundation), EPIC (Electronic Privacy Information Center), AILA (American Immigration Lawyers Association), Mijente, wrote to DHS Secretary Alejandro Mayorkas titled “Cancel DHS Use of AI Technologies for Immigration Enforcement and Adjudication by December 1, 2024.” They demanded suspension of AI tools noncompliant with OMB Memorandum M-24-10 (OMB AI governance memo). Categories “rights-impacting” and “safety-impacting” are formal risk classifications for AI systems. Source: Just Futures Law, confirmed by FedScoop’s reporting.
Diagnostic silence. Who is not speaking and why it matters
This is a diagnostic observation. As of May 25, 2026, an entire category of expected sources is silent.
Former senior DOJ immigration litigators are quiet
In Jan 2026, Sarah Lake Vuong (former Assistant Director, DOJ Office of Immigration Litigation) and Jess Ariela Dawgert (former Associate Deputy Attorney General, DOJ) formed Ariela Lake Law & Consulting in Denver. Both wrote and defended immigration policy in U.S. courts for years. They have not publicly commented on the AI topic.
Of 15 major think tanks, 14 are silent
Only Cato Institute (David Bier) published analysis. Migration Policy Institute, NFAP, American Immigration Council, Niskanen Center, AEI, Heritage, Brookings, Center for American Progress, FAIR, CIS, EPI, Bipartisan Policy Center, R Street, Manhattan Institute, Hoover — are silent.
Oddly, anti-immigration think tanks (Heritage, FAIR, CIS) that usually publish quickly are muted on AI in USCIS.
AILA publishes but did not file a targeted FOIA suit
AILA monitors and issues practice alerts but has not filed a specialized FOIA or litigation specifically about USCIS AI usage. AILA releases are advisory, not an institutional legal challenge.
What this means
The silence of insiders and think tanks may be due to:
- Perceiving the topic as a technical rebranding rather than a substantive change;
- Waiting for the courts (Pangea/Refugees) to produce documents before public commentary;
- Preparing confidential litigation strategies for clients where public comments would be counterproductive.
What the community and attorneys say publicly. Direct quotes from Reddit and LinkedIn
The most valuable stories are specific people describing what they saw in their RFE. Below are verbatim quotes from Reddit and LinkedIn threads gathered through direct research (Reddit search via API; Google search indexing for Reddit weakened in 2024). All quotes include URL, date, and upvote counts where applicable.
Main smoking gun. An RFE mentioning an employer the person never worked for
“What concerns me is that the RFE does not mention a single exhibit, achievement, or employer document I included. It is extremely general and even contains an employer name that has nothing to do with me… To assist in determining that the beneficiary has performed in a leading or critical role for FAANG, the petitioner may submit… For clarity: I have never worked for FAANG (the company named in the RFE).”
Translation: I’m concerned the RFE doesn’t reference any of my exhibits, achievements, or employer documents. It’s extremely general and even names an employer unrelated to me. I have never worked for FAANG. Source Reddit.
In thread comments, users suggested AI processing:
“They ran it through ai and they just shared result, I guess. Based on your profile, the tool took it you are from faang.”
Translation: They ran it through AI and just published the result. The tool deduced from your profile that you are from FAANG.
“My thoughts exactly. Also you can tell by the same 'This criterion has not been met because ...' for each criterion. What an interesting time we are living in.”
Translation: same phrase repeated for each criterion — typical template/AI pattern.
In another thread (“USCIS AI agent review”, Apr 30, 2026) someone asked directly whether Claude is used:
“do you think they use Claude?”
Translation: do you think they use Claude?
This is the first public mention of Anthropic Claude by a regular EB-1A filer. No direct confirmation in that thread, but the question shows where the community’s discourse is headed.
AILA immigration attorney publicly confirms the pattern
“USCIS misattributing submitted evidence to the wrong O-1 criteria - here my colleague suspected AI used by USCIS was hallucinating that the petition claimed criteria that they hadn't actually claimed.”
Translation: USCIS misattributed submitted evidence to wrong O-1 criteria — colleague suspected USCIS AI hallucinated criteria the petition did not claim. Source Reddit.
EB-5 firm received an RFE that reads like machine generation
“Several months ago, my firm received an RFE on an I-526e that heavily deviated from the typical RFE format. While we commonly see similar formats with a straightforward summary of the requested documents, on this occasion we saw a large conglomerate of patched together complaints with misspellings, incorrectly named entities, and dubious legal citations. It's possible some USCIS adjudicators may be incorrectly relying on unofficial AI software to do their work for them.”
Translation: our firm received an RFE on I-526e that deviated from typical format: a conglomerate of patched complaints with misspellings, misnamed entities, dubious legal citations — maybe some adjudicators rely on unofficial AI software. Source Reddit.
ELIS Evidence Classifier mis-tagging. Specific case of ignored criteria
“I submitted evidence for 6 criteria, but the RFE only addresses 4 of them. The other 2 aren't mentioned at all - not approved, not denied, just completely ignored as if I never submitted them.”
Translation: I submitted evidence for 6 criteria, but the RFE addresses only 4 — the other 2 are completely ignored as if never submitted. Source Reddit.
In comments the poster quotes their attorney, pointing directly to classifier operation:
“This happened to me, its really frustrating, my attorney says they have been having the same issue with many cases, it seems like when they scan the case when it arrives they don't scan the whole thing.”
Translation: my attorney says they have the same issue — it seems when the case is scanned upon receipt they do not scan the whole thing. This matches ELIS Evidence Classifier behavior discussed earlier.
Another comment:
“For my authorship criteria they said I submitted 'LinkedIn websites', whereas I never published anything on LinkedIn and never used that as proof (and what even does 'LinkedIn websites' mean?!)”
Translation: for my authorship criterion they said I submitted 'LinkedIn websites' — I never published on LinkedIn and never used it as proof. What does 'LinkedIn websites' even mean?!
Template-denial. 5 of 6 pages are a copy-paste of Policy Manual
“The denial letter is 6 pages, out of which 5 pages are a copy-paste of USCIS policy manual text. The denial letter only mentions my name and the first paragraph of my PE, and then it generally states that my PE has substantial merit but not national importance, without once mentioning any details regarding my PE or anything else from what I submitted in the RFE.”
Translation: a 6-page denial where 5 pages are copy-pasted from USCIS Policy Manual. The denial mentions only my name and first paragraph of my proposed endeavor, then states my endeavor has substantial merit but not national importance, without mentioning details I submitted. Source Reddit.
Author profile: PhD Electrical Engineering, postdoc at an R1 university, U.S. patent, 3 first-author papers in top-5 journals. Officer NSC 0399.
NOID after responding to RFE. Officer seems not to have opened the response
“Got NOID on April 21, 2026… But the NOID's objections reference ONLY original petition evidence. Not a single RFE exhibit is mentioned. NOID says 'Wikipedia printouts for associations' → We submitted actual bylaws and selection criteria that USCIS asked for in the RFE. Still says Wikipedia.”
Translation: I received a NOID referencing only the original petition evidence; none of the RFE response exhibits are mentioned. The NOID cites 'Wikipedia printouts' though we submitted bylaws and selection criteria. Source Reddit.
Author profile: Database Engineering Principal-level at a FAANG company, ~20 years experience, Guinness World Record, Fortune 500 tech adoption — none of these achievements were mentioned in the NOID.
Cluster of complaints about one officer. NSC 0592
In threads there is a stream of similar complaints about one Nebraska Service Center officer code 0592:
- u/ExpressionHappy5136: "same officer and officer only approved 2/3. Responded to the RFE and recently got denial."
- u/iaskgpt: "Same officer. Got 1/6 during RFE."
- u/BUTAIMO: "I'm on the same boat with Nebraska Center officer 0592. Got an RFE this Friday claiming 0 of the criteria were met."
- u/Grief_2022: "I am in the same boat with Nebraska officer 0438 who granted 0 out of 6 criteria."
A cluster of identical harsh denials from one officer may indicate either AI-driven clustering of cases by complexity or simply a single strict adjudicator. Distinguishing requires FOIA for the officer.
Balancing voice. Former USCIS supervisor
A critical source for balance: u/WatkinsImmigration — an immigration attorney and former USCIS supervisor. His post “A Fresh Perspective on USCIS After 6 Months on the Outside” (Apr 12, 2026, 109 upvotes) confirms sloppy adjudication patterns but refutes the thesis of direct AI adjudication:
“Adjudication: Poor, sloppy decisions and work being sent out by officers. This has been maybe the biggest shock to me so far. Even with using templates for large portions of writing, officers are still regularly misstating facts, forgetting to include large portions of required analysis, citing incorrect case law, and even leaving template language.”
Translation: adjudication is poor and sloppy; even with templates, officers misstate facts, omit required analysis, cite incorrect case law, and leave template language.
“AI: No, USCIS is not using AI to adjudicate your case. No, officers are not using it to write poorly written RFE/NOID/Denials. USCIS publicly lists their AI usage [link to dhs.gov/ai/use-case-inventory/uscis] and to the best of my knowledge, this is and remains accurate. I would look for expanded AI usage in vetting and evidence collection areas and cannot see any widespread usage for adjudication anytime soon.”
Translation: USCIS does not use AI to adjudicate cases or to write poorly written RFE/NOID/Denials. USCIS’s AI inventory is accurate; expect AI in vetting and evidence collection, not widespread adjudication use in the near term.
This source is important because he worked inside USCIS and is not gagged. His position: RFE patterns are explained by sloppy human officers using templates and insufficient training — a non-AI explanation. Yet he also notes unequal training resources:
“The NBC adjudication guide and training PowerPoint for new officers working the I-765 (c)(11) parole-based EAD category were over 100 pages and 200 slides long respectively. The I-140 EB-1A PowerPoint is 83 slides and new officers get a basic, 3 page long adjudication table.”
Translation: training materials for I-765 were >100 pages and 200 slides; I-140 EB-1A PowerPoint is 83 slides and new officers receive a basic 3-page adjudication table.
So an I-140 officer may have far less training than an EAD officer — a compelling non-AI explanation for sloppy decisions.
LinkedIn posts by attorneys — notable entries
Ksenia Maiorova. Direct statistics on AI-citations
“AI can summarize a decision. A lawyer understands what part of it is binding law. About ninety percent of cited cases do not actually support the proposition claimed. Once a case receives an RFE, approvals fall to roughly 30%. When the government misapplies the law, you need someone who knows how to challenge it.”
Translation: AI can summarize decisions, but lawyers know which parts are binding. About 90% of cited cases do not support the claimed proposition. After an RFE, approvals fall to ~30%. Source.
Maiorova lists LLM markers in RFEs: “opine”, “juxtapose”, “flowery language”, “aggressive wording” — atypical for USCIS officers but common to language models. Her firm’s internal statistics show much higher approval rates when law is properly applied.
Dobrina M. Ustun. A stark headline
“EB-1A and NIW approval rates have dropped below 50%. Let that sink in for a second. Half of these petitions are failing. We are at an absurd moment in immigration law — AI-generated petitions being reviewed by AI-generated RFEs, with a real human's life and career caught in the middle.”
Translation: EB-1A and NIW approval rates dropped below 50%; many petitions fail. We are in an absurd moment — AI-generated petitions reviewed by AI-generated RFEs, affecting real human lives. Source.
Ryan Wilck (Reddy Neumann Brown). January 2025 as a turning point
“AI hallucination occurs when an artificial intelligence system generates output that appears authoritative but is factually incorrect, internally inconsistent, or untethered from the actual input data... premium processing for discretionary petitions, particularly EB-1A and EB-2 NIW... has increasingly become a fast track not to approval, but to RFEs... paying $2,805 for an RFE that will not be substantively considered.”
Translation: AI hallucination means AI outputs authoritative-looking but incorrect content. Premium processing for discretionary petitions (EB-1A, EB-2 NIW) increasingly becomes a fast track to RFEs; paying $2,805 for an RFE that won’t be substantively considered. Source.
Wilck marks January 2025 as a turning point — coinciding with EO 14179 (Jan 23, 2025) and OMB guidance changes (M-25-21, Apr 3, 2025) that relaxed some prior Biden-era constraints.
Oleg Gherasimov (SG Legal). What AI does not do
“USCIS artificial intelligence is operational today. The systems described in the DHS inventory are not in testing. Human adjudicators retain final decision-making authority. AI systems do not grant or deny immigration benefits. These systems identify deviations from expected patterns. They do not evaluate your explanation.”
Translation: USCIS AI is operational today; systems listed in DHS inventory are not merely in testing. Human adjudicators retain final authority; AI does not grant or deny benefits. AI identifies deviations from expected patterns; it does not evaluate your explanatory narrative. Source.
Former asylum officer — direct workflow description
One of the most valuable insider pieces is Joshua Perez Garcia’s ILW.com article (May 11, 2026). His bio states he served six years as a federal adjudicator at USCIS as an Asylum Officer, Humanitarian Parole Officer, and Senior Refugee Officer. He now runs expert witness and AI compliance advisory work. Not an attorney.
“The flag does not announce itself as AI. There is no banner identifying the alert as the output of a machine-learning model.”
Translation: the alert does not identify itself as AI-generated. According to Perez Garcia, the alert appears to the officer as a normal Asylum Text Analytics notification highlighting matching textual passages — the officer does not see an explicit "this is AI" label. Source ILW.com.
“an alert created a bigger possibility of fraud, even where fraud might not be present, and that shifted the questions the officer was more likely to ask”
Translation: an alert increases perceived likelihood of fraud even where fraud may not exist, shifting the officer’s lines of questioning. This is an example of automation bias described by a verified former adjudicator.
“The officer has seen what the system surfaced; the applicant and attorney have not. The NOID does not disclose it. The denial letter does not disclose it. The administrative record may not disclose it.”
Translation: the officer sees what the system surfaced; the applicant and attorney do not. NOID/denial do not disclose it and the administrative record may omit it.
A second verified voice — Morgan Bailey (former USCIS) in Mayer Brown podcast (Dec 2025):
“The system is beginning to rely more on automation to organize information and detect patterns to support decision making by immigration officers, and AI is increasingly influencing what information reaches them and how that information is presented.”
Translation: automation organizes information and detects patterns to support decisions; AI increasingly influences what information reaches officers and how it is presented. Source Mayer Brown.
Third insider. Robert Ratliff, former Immigration Judge
Robert Ratliff — former Immigration Judge with 25+ years experience — published a client alert “Invisible Algorithms: The Hidden Role of Artificial Intelligence in USCIS Immigration Processing” (Feb 10, 2026). His background and client alert bring a judicial perspective on the due-process implications of automation.
Client alert headline: "Invisible Algorithms: The Hidden Role of Artificial Intelligence in USCIS Immigration Processing" — Robert Ratliff, former Immigration Judge. This is a formal client alert from a law firm. The fact a former judge publicly addresses "invisible algorithms" is a strong signal the issue is taken seriously inside the immigration legal ecosystem. Source.
If Perez Garcia describes the picture from an asylum officer’s operational level, and Morgan Bailey from USCIS staff, Ratliff addresses systemic effects from a judge’s vantage point. Key Ratliff theses:
“Artificial intelligence can affect which files are reviewed first, which issues are highlighted, how evidence is grouped, or which elements of an application receive greater attention. In cognitive science, this is often described as shaping the decision environment. The order in which information is presented and the signals associated with that information can influence human judgment.”
Translation: AI affects which files are prioritized, which issues are highlighted, how evidence is grouped, and which application elements receive more attention — shaping the decision environment and influencing human judgment. Source.
This is the core mechanism: AI does not decide but creates an information environment that shifts human judgment.
Ratliff cites scope-scale data explaining institutional pressure toward automation:
“As of Q3 FY2025 (April–June 2025), USCIS reported a pending application number of over 11 million cases across all application types.”
Translation: In Q3 FY2025 USCIS had over 11 million pending applications across all types — a scale that explains why the agency must adopt automation even if governance is immature.
Ratliff also references AILA’s position that inconsistent adjudication patterns exist where reasoning in the record does not always match submitted evidence — supporting the claim that systemic problems predate AI.
Three insiders present one pattern:
- Joshua Perez Garcia (former asylum officer) — at the officer level: ATA alert shifted lines of questioning and raised fraud likelihood even where fraud might not exist.
- Morgan Bailey (former USCIS staff) — at the organization level: AI increasingly influences what reaches officers and how it’s presented.
- Robert Ratliff (former Immigration Judge) — at the system level: AI shapes the decision environment via ordering, highlighting, and grouping information, thus affecting outcomes even when the final decision rests with humans.
This is not three isolated complaints but the same phenomenon described from multiple hierarchical perspectives. The convergence of independent insiders is stronger evidence than anecdotal lawyer reports.
Legal precedent: Mukherji v. Miller
This could be the most important legal development. Mukherji v. Miller et al., No. 4:24-cv-03170 (D. Neb.) — journalist Anahita Mukherji’s EB-1A case — decision on Jan 28, 2026 by Senior Judge Joseph F. Bataillon. Defendants: Loren K. Miller and Ur Mendoza Jaddou (USCIS officials in their official capacities).
Mukherji v. Miller (D. Neb., Jan 28, 2026)
Federal court found USCIS application of the "final merits determination" under the Kazarian two-step EB-1A test to be arbitrary and capricious — a legal basis under the APA to vacate agency action. The court held certain policy elements (e.g., a "recency requirement") lacked statutory basis and were adopted without APA notice-and-comment. The decision relied on Kazarian v. USCIS and post-Loper Bright doctrine. DOJ filed a notice of appeal March 27, 2026 to the 8th Circuit (case No. 26-01578).
Why this matters: post-Loper Bright (2024), courts need not defer to agency statutory interpretations (Chevron deference is weakened). Mukherji opens a path to challenge any non-transparent AI-related USCIS decisions under the APA as arbitrary and capricious. DOJ has appealed; the appeal is pending as of May 2026.
What cited USCIS cases actually are
Separately, The Seltzer Firm documented systematic miscitation of cases in RFEs for EB-1A/O-1. USCIS frequently cites:
- Silverman v. Eastrich Multiple Investor Fund, L.P., 51 F.3d 28 (3d Cir. 1995) — a credit-default case, unrelated to immigration, yet cited regarding "original contributions of major significance."
- APWU v. Potter, 343 F.3d 619 (2d Cir. 2003) — about anthrax investigation; cited in EB-1A RFEs.
- Visinscaia v. Beers, 4 F.Supp.3d 126 (D.D.C. 2013) — about a Moldovan ballerina; its holding is mischaracterized in RFEs.
- Matter of Caron International, 19 I&N Dec. 791 (Comm. 1988) — selectively quoted out of context.
Maybe these are officer templates; maybe PAiTH hallucinated; maybe a mixture. We can’t decide without leaked prompts.
Detailed Reddit analysis. Specific RFE patterns in AI-related fields
I’ve systematized Reddit community reporting on AI-related petitions and RFEs. All URLs were verified via old.reddit.com on May 25, 2026 — these are real threads.
“Be careful using AI.” Debates among former USCIS staff
In July 2025 u/WatkinsImmigration reposted a Bluesky screenshot from a former USCIS colleague now handling EB-1 petitions. Quote from the Bluesky screenshot:
“According to two attorneys and my employer's legal representative, USCIS is utilizing artificial intelligence to evaluate applications and issue Requests for Evidence (RFEs) as the premium processing period comes to a close.”
Translation: According to two attorneys and my employer’s legal rep, USCIS uses AI to evaluate applications and issue RFEs as premium processing nears completion. Source Reddit.
Watkins replied in the thread that those attorneys are wrong and pointed to the DHS inventory listing AI usage.
This exchange captures the community split: some claim AI writes RFEs now, others (including insiders) point to the official inventory where adjudication AI is not listed as making final decisions.
“AI Detection systems on your application.” One documented case
“I have only seen one RFE complaining about 'possibly AI generated articles' but it can be refuted... I haven't observed USCIS formally identifying anyone for using AI-generated material, and to be honest, the reliability of AI detection tools is questionable at best.”
Translation: I’ve seen only one RFE alleging 'possibly AI-generated articles' — it was refutable. I have not seen USCIS formally identify anyone for using AI-generated material; AI detectors are unreliable. Source Reddit.
So as of May 25, 2026, Reddit shows only one RFE directly accusing AI-generated articles — and that case was successfully challenged.
“RFE misclassification issue.” USCIS labelled a field as “Science”
“USCIS misclassified my field as 'Science' instead of my actual field, Artificial Intelligence — which distorted their interpretation of citations.”
Translation: USCIS misclassified my field as 'Science' instead of my actual field, Artificial Intelligence, distorting how they interpret citation counts. Source Reddit.
This matters because citation benchmarks differ across fields. ML classifier labeled the field incorrectly and the officer compared citation counts to inappropriate norms.
“Split endeavor.” A parsing error from commas
“The endeavor had few connected fields but separated by comma and it took them as 3 different endeavours. So the main point was: 'any of your evidence cover all these 3 endeavours.'”
Translation: because my proposed endeavor listed several related areas separated by commas, the parser interpreted them as three separate endeavors — and the RFE claimed none of your evidence covered all three. Source Reddit.
This looks like a parsing/NER error rather than human judgment.
r/EB2_NIW. Rising denials since Jan 2025
In Apr 2025 r/EB2_NIW thread “What’s going on with NIW recently?”:
“A PhD in Computer Science, employed at a FAANG company in a research role, with over 100 citations — still received a three-prong RFE.”
Translation: a PhD in CS at FAANG with 100+ citations still received a three-prong RFE. Source Reddit.
In Aug 2025 thread “Denials and More Denials Happening”:
“EB-2 NIW denial rates hit 57% in 2025! USCIS officers have complete discretion to deny any case.”
Translation: EB-2 NIW denial rates reached 57% in 2025. Source Reddit.
Community explanations vary: one claims many self-petitioners use AI to draft weaker submissions; others blame USCIS adjudication changes.
Balancing case. An AI-related petition approved without RFE
In Mar 2026, thread “I-140 Approved on 43rd Business day”:
“Being in AI does NOT automatically mean RFE. A late decision in premium processing does NOT mean RFE.”
Translation: being in AI field does not automatically trigger an RFE; a late premium-processing decision does not necessarily mean an RFE. Source Reddit.
An important balancing voice: AI sector is not automatically doomed.
r/I130Suffering. A list of what USCIS AI does
In Dec 2025 r/I130Suffering thread “USCIS has ADMITTED to Using AI to Evaluate I-129F/I-130 Petitions” (family-based visas) described functions applicable across form types:
“AI is notorious for generating false positives — a bad thing for applicants since this means false flags. Any submission that triggers an AI alert can create obstacles long before a human actually reviews your case.”
Translation: AI tends to generate false positives causing false flags. An AI alert can create obstacles before a human reviews the case. Source Reddit.
Thread lists AI functions USCIS performs:
- Document Categorization — automatic tagging;
- Pattern Detection — matching "scripted stories" across unrelated cases → fraud flags;
- Relationship Linkages — network analysis: addresses, employers, relatives;
- Inconsistency Flagging — cross-checking dates/facts against government databases → RFE.
Community proposals. Visalytics — community-built analytics
In Apr 2026 thread “I scraped 2,500+ EB-1A petition decisions from USCIS”:
“I'm a data engineer working on my own immigration case. I scraped AAO appeal decisions from USCIS and thousands of approved case studies... It matches you to similar decided cases, demonstrating how different profiles yield different results.”
Translation: a data engineer scraped AAO appeal decisions and built a tool (Visalytics.com) to match your profile to similar decided cases, showing how different profiles yield different results. Source Reddit.
Visalytics is an example of the community building its own ML tools to understand USCIS patterns — essentially AI vs AI.
Summary table of AI-related RFE patterns
- Myth "AI field = automatic RFE" — refuted by approval cases. Quality of argument matters, not the field.
- Field misclassification (AI → "Science") — documented in r/eb_1a (Jan 2026). Remedy: first section of RFE response must re-establish field context.
- Split endeavor due to commas — parser error recorded in r/eb_1a (Apr 2026).
- "AI-generated articles" allegation — only one RFE recorded and successfully rebutted (Oct 2025).
- National importance (Dhanasar prong 2) — a main trigger. Need evidence of adoption, economic impact, alignment with national AI initiatives.
- False positives from ELIS Classifier — documented by Cozen O'Connor. Countermeasure: clear structure, explicit Table of Contents.
- Revocation of previously approved cases — reported; avoid predatory journals and fake conferences.
Key takeaways from 19 reviewed threads
- USCIS uses AI at intake, pattern detection, and identity matching. This is uncontested by both supporters and skeptics.
- AI does not evidenceably make final decisions on cases. Former USCIS staff deny such use.
- Officers do flag "AI smell" (boilerplate, generic phrases). Avoid that by customizing and providing original substance.
- Field misclassification (AI labeling you as "Science") is real.
- Denial spikes in 2025: NIW up to 57% in some months; EB-1A under heightened scrutiny. AI alone is not proven as the cause.
- Applicants using AI for drafts is common; community sees AI as a drafting tool, not a finalizer.
Q3 FY2025 numbers. Hypothesis: AI “ate” the discretionary part of adjudication
USCIS Q3 FY2025 statistics show an asymmetry consistent with the hypothesis that AI affected discretionary adjudication.
Q3 FY2025 (Apr–Jun 2025):
EB-1A approval rate: 66.6% — a 3-year low, down 8.4 percentage points quarter-over-quarter
EB-2 NIW approval rate: 54% — down 13 points
O-1 approval rate: 93.8% — essentially unchanged (RFE rate 18.7% YTD vs 30% in 2020)
Q4 FY2025 (Jul–Sep 2025) — worse per Manifest Law (USCIS performance data):
EB-1A: 53.4% (2,331 approved / 4,364 adjudicated) — "the lowest quarterly approval rate for EB-1A in recent years"
EB-2 NIW: 35.7% (2,968 approved / 8,324 adjudicated) — "more petitions were denied than approved for the first time in recent memory". This is the first modern instance where NIW denials exceeded approvals.
Quarterly NIW dynamics in FY2025: Q1 62.7% → Q4 35.7%. Full FY2025 drop from 95.7% in FY2022 to 55.2% — a near threefold collapse.
⚠️ Causation vs correlation
The decline is real and systemic. But attributing it solely to AI adoption is scientifically premature. At least four competing explanations exist simultaneously:
- AI intervention in discretionary adjudication. This is the hypothesis developed in this analysis: AI systems affect discretionary assessment in EB-1A/NIW. Correlation between AI rollout and approval drops exists, but FOIA evidence is needed for causation.
- NIW boom 2022–2024 — influx of weaker self-petitions. After Matter of Dhanasar (2016) liberal interpretation and subsequent trends, many marginal self-petitions entered the pool, lowering approval rates.
- Policy shift under the Trump-era EO Jan 2025. EO 14179 and OMB memos in 2025 changed federal AI and policy environment; enforcement posture may have tightened.
- Service Center reorganizations centralizing EB-1A. Operational shifts can lengthen adjudication and affect outcomes independent of AI.
Without FOIA releases it is impossible to quantify each factor’s contribution. This article shows AI systems exist and that approval declines coincide with their expansion — a serious correlation but not definitive proof.
What’s odd in the numbers. EB-1A and EB-2 NIW require discretionary judgment: the officer must assess whether the applicant meets standards of “extraordinary ability” or “national importance” based on the totality of evidence. These are qualitative determinations. O-1 is more mechanical (checklist). AI/ML handles mechanical checks better than nuanced qualitative assessments. The drop in discretionary categories alongside stability in checklist categories fits the hypothesis that AI negatively impacted discretionary evaluation.
⚠️ Logically follows (my inference, not DHS quote)
This asymmetry aligns with the idea that AI systems do well at verifying formal criteria (O-1) but poorly at qualitative judgments (EB-1A, NIW). ELIS Evidence Classifier tags documents; PAiTH may draft case-law analysis. For discretionary categories this creates systematic risk of mis-tagging and undervaluation of strong evidence. This is not proof, but a plausible correlation.
Specific AI patterns observed in legal practice. What lawyers actually see
Beyond complaints, lawyers document concrete technical patterns best explained by automation.
Same-day RFE. Technically impossible for manual review
Herman Legal Group in “USCIS artificial intelligence 2026”:
“In several concurrent adjustment filings — including Form I-485, Form I-130, Form I-864, Form I-765 — we received receipt notices and RFEs issued the same day.”
Translation: in multiple concurrent AOS filings we received receipt notices and same-day RFEs.
“The emergence of same-day RFEs — issued effectively simultaneously with receipt generation — suggests something different: Automated intake screening may be parsing I-864 data immediately upon digitization.”
Translation: same-day RFEs imply automated intake screening parsing forms immediately upon digitization.
In plain terms. A receipt notice and an RFE on the same day is technically impossible for human review. An RFE issued hours after receipt indicates the RFE was generated automatically during intake parsing.
Officer codes as a community metric. Case TSC XM2532
The Reddit community tracks allegedly problematic officers by their internal codes (XM-codes). This helps show whether AI drafts are later approved consistently or adjusted variably by humans. (Thread cut off here.)"side-line" /div
div
div data-es="quote-label" WeGreened Law Group, success story по NIW /div
blockquote “On March 22, 2024, USCIS issued a Request for Evidence (RFE) signed by Officer XM2532, strong challenging all three prongs of the NIW test /strong … The AAO returned the case to the original service center for reconsideration on April 4, 2025, effectively vacating the initial denial… the client got his I-140 NIW approval on July 9, 2025.” /blockquote
p data-es="quote-note" Translation: On March 22, 2024 USCIS issued an RFE signed by Officer XM2532 (TSC - Texas Service Center, one of USCIS regional service centers), challenging all three prongs of the NIW test (Dhanasar). The AAO (Administrative Appeals Office - USCIS internal appeals body) returned the case to the service center for reconsideration on April 4, 2025, effectively vacating the initial denial. The client received I-140 NIW approval on July 9, 2025. a href="Success Stories: NIW Case Approved Following the Appeal of a Denial Decision by TSC Officer XM2532" Source: WeGreened /a . /p
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Client profile: a postdoc researcher integrating deep learning and numerical modeling to forecast coastal hazards — i.e., an AI/ML expert working on disaster mitigation. And yet Officer XM2532 denied on all three prongs. The AAO returned the case for reconsideration after 13 months.
What this means at the article level. The Reddit community tracks patterns by officer codes because — if all officers applied standards identically — there wouldn’t be clusters of denials tied to a single officer. This is either the human bias of one person, or an AI tool that this officer uses more often than others. You can’t distinguish without a FOIA on that specific officer.
Greenberg Traurig. The shortest formulation of what’s happening
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div data-es="side-line" /div
div
div data-es="quote-label" Kate Kalmykov, Co-Chair Global Immigration Compliance Practice, Greenberg Traurig, February 2026 /div
blockquote “The future of U.S. immigration adjudications is not just digital - it is algorithmic.” /blockquote
p data-es="quote-note" Translation: the future of immigration adjudication in the U.S. is no longer just digital — it is algorithmic. a href="The U.S. Government’s Expanding Use of AI in Immigration Adjudications: Considerations for Employers and Investors | Insights | Greenberg Traurig LLP" Source: Greenberg Traurig /a . /p
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This is one of the largest U.S. law firms (Greenberg Traurig — Top 15 by revenue). When a source like that publicly says “algorithmic,” it means they’ve already accumulated enough confirmations within their client base to make that claim public.
Mukherji v. Miller. Details that show the scale
Returning to a case I’ve written about before, now with specifics that weren’t previously public. The plaintiff — Anahita Mukherji, an Indian journalist — met 5 of the 10 EB-1A regulatory criteria — i.e., 1.6 times the formal minimum (you need 3). USCIS still denied.
Reddy Neumann Brown’s analysis of the decision:
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div data-es="side-line" /div
div
div data-es="quote-label" Reddy Neumann Brown PC, analysis of Mukherji v. Miller, January 2026 /div
blockquote “the court ordered USCIS to strong approve the applicant’s EB-1A petition outright, rather than send the case back /strong ” /blockquote
p data-es="quote-note" Translation: the court ordered USCIS to approve the applicant’s EB-1A petition outright, rather than remand the case for reconsideration. — This is an extremely rare judicial outcome. Courts usually remand with instructions. “Outright approval” means the judge found the denial so arbitrary that they didn’t even trust USCIS to correct it. /p
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The DOJ filed a notice of appeal on March 27, 2026 in the U.S. Court of Appeals for the 8th Circuit, case No. 26-01578. The decision is currently at the district/circuit level — but as a precedent it’s the first time since Loper Bright (2024) that a federal court told USCIS “you didn’t just err in one case, your EB-1A evaluation system is itself arbitrary and capricious.” On appeal the 8th Circuit may either affirm (which would strengthen the precedent regionally) or reverse (which would restore the Kazarian framework as before). A decision is expected in 2026–2027.
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a name="dhs-context" /a
Why I look at ICE and CBP when I write about USCIS
One common defense of USCIS is: “AI is only auxiliary — PDF processing, translation, classification. We’re far from substantive petition adjudication.” To assess how robust that claim is, you need to look at neighboring DHS components. If in ICE and CBP the official formula “AI only assists humans” has already been eroded down to facial recognition without privacy assessments and to LLM-processing of tips — that changes the forecast for USCIS.
The three facts below are not directly about EB-1/O-1/NIW. But they show DHS’s methodology — how AI systems are deployed, how they’re disclosed, and how likely it is that something similar already operates inside USCIS adjudication but hasn’t been publicly revealed.
Hurricane Score. The same formula used to defend AI in USCIS
In November 2024 DHS Chief AI Officer Eric Hysen, in response to a letter from Just Futures Law (the same organization that filed Pangea v. USCIS), disclosed the existence of the Hurricane Score — an algorithm that ranks immigrants on a scale from 1–5.
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div data-es="quote-label" Eric Hysen, DHS Chief AI Officer, letter to Just Futures Law, November 2024 /div
blockquote “The Hurricane Score does not make decisions on detention, deportation, or surveillance; instead, it is used to strong inform human decision-making /strong .” /blockquote
p data-es="quote-note" Translation: The Hurricane Score does not decide detention, deportation, or surveillance; instead, it is used to inform human decision-making. a href="Surveillance tech advances by Biden could aid in Trump's promised crackdown on immigration - ABC17NEWS" Source: AP via ABC17 News /a . /p
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strong Why this matters for EB-1/O-1/NIW. /strong The formula “AI assists humans, final decision by a human” is the same formula DHS uses to defend ELIS Evidence Classifier, PAiTH Legal Persona, and any other system touching your petitions. Hurricane Score shows that in practice this formula has already been eroded: the algorithm assigns a score, officers tend to agree, and the balance shifts. Joshua Perez Garcia described exactly this in his article — automation bias is a real mechanism, not a theoretical threat. For an EB-1A filer this means that when USCIS says “PAiTH only advises the officer,” we already have a precedent in a sister DHS system where “advice” becomes de facto decision-making.
Mobile Fortify. The “hidden deployment → disclosure” methodology applies to USCIS
In January 2026 Wired published an investigation based on the DHS 2025 AI Use Case Inventory: ICE and CBP had used the Mobile Fortify app for facial recognition since June 2025. The vendor is Japanese NEC (NeoFace). Contract ~$23.9M. Used 100,000+ times before public disclosure.
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ul
li Database: strong 1.2 billion face photos /strong for matching; /li
li Privacy Impact Assessment strong was not conducted /strong before deployment; /li
li Photos of U.S. citizens are also stored — strong for 15 years /strong ; /li
li A lawsuit by the State of Illinois and the City of Chicago over unlawful use has already been filed. /li
/ul
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Sources: a href="Mobile Fortify - Wikipedia" Wikipedia with Wired citations /a , a href="ICE facial recognition app Mobile Fortify powered by NEC | Biometric Update" Biometric Update /a , a href="ICE drives AI use case growth within Homeland Security | FedScoop" FedScoop /a .
strong Why this matters for EB-1/O-1/NIW. /strong Mobile Fortify is enforcement (ICE/CBP), not adjudication (USCIS). But the important point is the disclosure scenario: a high-risk AI system is deployed without a PIA, runs quietly for 7+ months, and surfaces through a combination of (1) the annual AI inventory update, (2) leaked emails to 404 Media, (3) a Wired investigation, (4) lawsuits. If USCIS deploys something analogous inside adjudication for EB-1A/NIW (for example, a classifier that influences final merits determinations more than claimed) — it will likely surface via the same route in the next 12–24 months. Active FOIA suits Pangea v. USCIS and Refugees International v. USCIS are exactly the part of that path already in motion.
AI Enhanced ICE Tip Processing. A precedent of using commercial LLMs in immigration workflows
From the same DHS AI Inventory (January 2026) — ICE has used a Palantir tool since May 2025 to process the flow of anonymous tip submissions from the public:
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div data-es="cfr-card-header" a href="ICE drives AI use case growth within Homeland Security | FedScoop" FedScoop, January 2026, based on the DHS AI Inventory /a /div
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blockquote “It uses generative AI to summarize public tip submissions, translate messages written in other languages, and generate short ‘bottom line up front’ summaries that help agents decide which tips require immediate attention… DHS records say the system relies on strong commercially available large language models trained on public data, with no additional training on ICE-specific records /strong .” /blockquote
p Translation: it uses generative AI to summarize tip submissions, translate messages from other languages, and generate short “bottom line up front” summaries that help agents decide which tips require immediate attention. DHS says the system relies on commercially available LLMs trained on public data, with no additional training on ICE-specific records. /p
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strong Why this is critically important for EB-1/O-1/NIW. /strong ICE officially uses commercial LLMs (the same class as ChatGPT, Claude, Gemini) inside an immigration workflow for classification and summarization. This creates an institutional precedent that commercial LLMs are permissible within DHS workflows. PAiTH Legal Persona at USCIS (currently Pre-deployment) performs functionally the same tasks — generating petition document summaries and draft legal memoranda. When USCIS moves PAiTH from Pre-deployment to Deployed (a matter of months, not years) — architecturally the only remaining boundary between AI-processing of ICE tips and AI-processing of your EB-1A petition will be the formal claim “a human makes the final decision.” The same boundary is already eroding in the Hurricane Score case.
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p strong What this gives you for an article on EB-1/O-1/NIW /strong /p
ul
li The DHS formula “AI only assists humans” is the standard defense, but in the Hurricane Score case it doesn’t hold in practice (automation bias). The same formula is applied to ELIS Classifier and PAiTH at USCIS. /li
li Commercial LLMs (ChatGPT-class models) are officially used in DHS workflows as shown by ICE tip processing. A precedent exists. PAiTH for USCIS is the next logical step. /li
li High-risk DHS AI systems can be deployed without a Privacy Impact Assessment and operate quietly for years (Mobile Fortify). If USCIS does something similar for adjudication of EB-1A, disclosure will likely follow the same path (inventory + leaks + FOIA + litigation) within 12–24 months. /li
/ul
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p strong ⚠️ What I COULD NOT verify — try yourself /strong /p
p There were claims in source materials I couldn’t find at working URLs. If verified links are available I’ll add them: /p
ul
li strong Judge Sara Ellis (US District Court Northern District of Illinois) and ChatGPT in a use-of-force report /strong — allegedly November 2025, AP. Search Google News for “Sara Ellis ChatGPT use of force ICE” + PACER. This isn’t about EB-1/O-1, but it’s a precedent that ChatGPT has already appeared in official immigration documents. /li
li strong Reuters on USCIS enhanced FBI NGI background checks from April 27, 2026 /strong . Search reuters.com for “USCIS FBI NGI fingerprint background checks April 2026” or check uscis.gov news releases April 2026. This isn’t AI, but it is an automated screening layer added between petition filing and decision. /li
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a name="practice" /a
What to do right now. Practical takeaways for EB-1A, O-1, EB-2 NIW filers
These recommendations work regardless of whether your petition is processed by AI or a human. They protect against both scenarios.
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strong File the dossier in a format optimized for ML classifiers /strong
p Clear exhibit tabs (separate tab for each supporting document) with labels in English. Each piece of evidence as a separate PDF or a clearly separated section. Include a Table of Contents with explicit categorization by the 10 Kazarian criteria (for EB-1A) or the 3 prongs of Dhanasar (the three-prong Dhanasar test for EB-2 NIW: substantial merit + national importance, well-positioned to advance, balance of factors favors waiver) or the 8 O-1 criteria. /p
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strong File names are critical /strong
p Use code Recommendation_Letter_Dr_Smith_Harvard.pdf /code , not code scan_001.pdf /code . The ELIS Classifier uses file names and content for tagging. /p
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strong OCR layer on all PDFs /strong
p An OCR layer is a text layer the computer can read as text, not an image. No “ghost text” (hidden text — Cozen explicitly calls this a trigger for cross-document mismatch). Minimum 300 DPI for scans. /p
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strong Attach certified human translations /strong
p Do not rely on USCIS internal Azure translation. Include certified human translations with a certificate of accuracy (a translator’s formal document attesting the translation is accurate) per a href="8 CFR § 103.2 - Submission and adjudication of benefit requests. | Electronic Code of Federal Regulations (e-CFR) | US Law | LII / Legal Information Institute" 8 CFR § 103.2(b)(3) /a for Russian awards, diplomas, media publications. This is legally required AND neutralizes the risk of AI mistranslation. /p
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div data-es="conclusion-num" 5 /div
strong Russian name transliterations /strong
p Include in each petition an explicit name-variation memorandum listing all transliteration variants according to GOST (Russian standard), ISO 9 (international standard), and BGN/PCGN (U.S. Board on Geographic Names standard used in American passports). Explain possible false matches in ATLAS in advance. /p
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strong Consistent job titles across ALL documents /strong
p Cross-document mismatches trigger flags. /p
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strong EB-1A with premium processing — risky in 2026 /strong
p Per Reddy Neumann Brown’s recommendation — avoid premium processing except for critical deadlines. /p
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div data-es="conclusion-num" 8 /div
strong If you receive an RFE — verify EVERY cited case law /strong
p A pattern of systematic miscitation (Silverman, APWU, Visinscaia, Caron International) is documented. This may be a PAiTH hallucination. /p
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div data-es="conclusion-num" 9 /div
strong Use DHS AI Use Case Inventory wording /strong
p In each DHS Inventory entry the AI tool is labeled a “decision support tool” — not as an autonomous decision-maker. In responding to an RFE you can cite the DHS inventory and demand that the final decision be made by an officer considering all submitted evidence, not delegated to a machine. /p
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a name="watch" /a
What I will watch in the coming months
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div data-es="doc-card-header" div data-es="doc-num" 1 /div strong DHS AI Use Case Inventory /strong /div
p Updates twice a year. Especially: whether PAiTH Legal Persona appears in Deployed status (currently Pre-deployment). And whether ELIS Evidence Classifier is reclassified from a routine system to High-Impact. /p
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div data-es=“doc-card” data-es-mod="2"
div data-es="doc-card-header" div data-es="doc-num" 2 /div strong Pangea v. USCIS and Refugees International v. USCIS /strong /div
p Productions may contain prompts, training data, contracts. Any court order in these cases will be an inflection point. /p
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div data-es=“doc-card” data-es-mod="3"
div data-es="doc-card-header" div data-es="doc-num" 3 /div strong Cozen O’Connor, Reddy Neumann Brown, Greenberg Traurig /strong /div
p Watch for the next RFE alerts. If patterns intensify — it means AI is being applied more widely. /p
/div
div data-es="doc-card"
div data-es="doc-card-header" div data-es="doc-num" 4 /div strong Reddit r/EB1, r/USCIS, Trackitt /strong /div
p The first RFE reports that cite nonexistent case law (a smoking gun for AI hallucination in RFEs). /p
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div data-es=“doc-card” data-es-mod="2"
div data-es="doc-card-header" div data-es="doc-num" 5 /div strong SAM.gov and USAspending.gov /strong /div
p New USCIS contracts with AI vendors. Especially: whether the VOWS Palantir contract (marriage fraud) will be expanded to employment-based categories. /p
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div data-es="doc-card-header" div data-es="doc-num" 6 /div strong USCIS GitHub /strong /div
p a href="USCIS · GitHub" USCIS · GitHub /a — currently nearly empty. If repositories with ML models appear — that will be an unexpected self-disclosure. /p
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a name="summary" /a
Short summary
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strong USCIS officially uses AI/ML in petition processing /strong
p This is publicly confirmed by the DHS AI Use Case Inventory: 29 USCIS AI use cases. Anthropic Claude processes PDFs at intake via myUSCIS, Microsoft Azure translates foreign documents, and an ML classifier tags your evidence. /p
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strong DHS’s main promise /strong
p AI does not make final decisions. This is explicitly stated in the DHS AI Use Case Inventory: each system is labeled a “decision support tool.” There are no public leaks contradicting this. /p
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strong Where AI actually intervenes in EB-1A/O-1/NIW /strong
p Intake (Anthropic Claude) → identity resolution (PCIS) → fraud screening (ATLAS) → evidence classification (ELIS Classifier) → translation (Azure) → officer assistance (PAiTH Legal Persona). /p
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strong Lawyers have been logging AI-pattern RFEs since January 2025 /strong
p But the same patterns are equally explained by OCR errors and human templates. Without leaked prompts we cannot distinguish. /p
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strong Two FOIA suits are active /strong
p Pangea v. USCIS and Refugees International v. USCIS. If courts rule for the plaintiffs — we will get the first real documents. /p
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strong Former DOJ insiders and 14 of 15 major think tanks are silent /strong
p Only Cato (David Bier) has published its own analysis. That’s a diagnostic signal. /p
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div data-es="conclusion-num" 7 /div
strong Main practical takeaway /strong
p Prepare your petition so it is equally well processed by an AI classifier and a human officer: clear file names, OCR layer, certified translations, name variation memorandum, consistent job titles. This protects against both scenarios. /p
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