USCIS and AI 2026 — a topic discussed daily on forums by people filing EB-1A, O-1 and EB-2 NIW. People write “AI is scanning my petition”, “Claude reads my recommendation letters”, “the officer just trusts what the model showed”. Good news: no need to guess. DHS itself publishes a list of its AI systems by law, there are FOIA suits texts, a federal court decision, and dozens of public applicant stories. In this pillar overview I collected what is confirmed by official documents and working links — and showed what AI at USCIS does, what it doesn’t do, and what that means for the petitioner.
This is a pillar overview. Details are disclosed in three related articles below.
Short verdict
USCIS officially uses AI: as of January 28, 2026, the DHS AI Use Case Inventory lists 29 USCIS AI use cases. Anthropic Claude 3.7 Sonnet processes PDFs during online filing via myUSCIS, Microsoft Azure translates foreign documents, and an ML classifier tags pieces of evidence. DHS’s main promise (direct Reuters quote, May 2024): “AI will not be making immigration decisions”. There are no public leaks disproving that. But attorneys have been documenting RFEs with signs of AI-generation since January 2025, two FOIA suits seek to disclose prompts and contracts, and in January 2026 a federal court in Mukherji v. Miller for the first time vacated a USCIS EB-1A denial relying on Loper Bright.
Contents of the pillar overview
Why “USCIS and AI” became a hot topic in 2026
Is USCIS using artificial intelligence to process my EB-1A, O-1 or EB-2 NIW petition in 2026?
When someone in immigration Telegram channels writes “USCIS runs petitions through AI”, the usual reaction is an eye-roll. Sounds like a conspiracy theory. In fact USCIS disclosed this publicly because it is legally required to. Under the Advancing American AI Act (S.1353, 117th Congress) each federal agency must maintain and publish an inventory of its AI systems. DHS does this at United States Citizenship and Immigration Services – AI Use Cases | Homeland Security. The latest update is January 28, 2026, and it lists 29 USCIS AI use cases.
In 2026 the discussion moved from “they say” to “here are the documents”. Three events put the topic into the factual realm.
- The DHS AI Use Case Inventory — the federal registry of AI systems that the Department of Homeland Security must publish. The January 2026 version lists 29 use cases for USCIS, each indicating vendor, model, status, and application area.
- Two FOIA suits — Pangea Legal Services v. USCIS (1:24-cv-02809-ACR) and Refugees International v. USCIS (1:24-cv-03559) — both filed in the US District Court for the District of Columbia, both before Judge Ana C. Reyes, both seeking USCIS records on AI use in adjudication.
- The Mukherji v. Miller decision (D. Nebraska, January 28, 2026) — a federal court for the first time vacated a USCIS EB-1A denial relying on Loper Bright Enterprises v. Raimondo (2024), which eliminated Chevron deference.
“The GenAI powered library utilizes Amazon Bedrock - Anthropic Claude 3.7 Sonnet V1 Foundation Model to extract data from PDF forms.”
This is the literal text from the DHS public registry, not a paraphrase. The GenAI PDF Intake library uses Claude 3.7 Sonnet via AWS Bedrock to extract data from PDF forms. Source: dhs.gov/ai/use-case-inventory/uscis.
This frames the whole conversation. Some in the community are sure AI is writing RFEs right now. Others, including former USCIS insiders, point to the official list where adjudication-AI simply isn’t present. The truth lies in between: AI is used at intake, translation, classification and fraud-screening stages — but not as the entity that approves or denies.
Three facts confirmed by documents
I checked every claim against a working URL. If a link leads to 404 — I couldn’t confirm it and I remove the claim from the overview.
Use case DHS-2598 “PDF Intake (PDFI) for myUSCIS”, status Deployed. Purpose — extracting data from uploaded PDF forms. This is not a petition classifier; it is intelligent document processing based on an LLM.
Source: DHS AI Use Case Inventory, January 2026 update.
Pangea Legal Services v. USCIS (1:24-cv-02809-ACR) and Refugees International v. USCIS (1:24-cv-03559) — both before Judge Ana C. Reyes. Per the Joint Status Report of March 31, 2026 USCIS completed production on June 30, 2025, but the most sensitive documents (prompts, training data) have not yet been released. A Vaughn Index has not been issued.
Source: Just Futures Law — DHS AI FOIA.
EB-1A: 66.6% approvals — the lowest in 3 years. EB-2 NIW: 54%. O-1 for contrast: 93.8% and steady. The drop is exactly where qualitative judgment, not a checklist, matters.
What I am not claiming
A direct causal link between AI deployment and the decline in approval rates is not proven by official data. USCIS publicly states that AI does not make decisions. But the three facts above are real, verified, and occurred in the same time window. And the asymmetry (discretionary EB-1A and NIW collapsed while checklist-driven O-1 held steady) is consistent with the hypothesis that AI tools perform worse where qualitative judgment about the significance of contributions is required.
“USCIS has not published any error-rate data, and practitioners report RFEs for documents that were in fact submitted, consistent with classifier mis-tagging.”
USCIS does not publish error-rate data, and practitioners report RFEs for documents that were actually submitted — consistent with classifier mis-tagging. Source: Cozen O'Connor.
Is AI at USCIS making decisions on EB-1A and NIW
The strongest official rebuttal to the panic “AI decides my case” is DHS’s direct response to Reuters in May 2024.
“AI will not be making immigration decisions. USCIS is building an AI program that will tailor training materials to officers and help them make more accurate decisions, with knowledge of country-specific conditions and other reference information.”
AI will not be making immigration decisions. USCIS is building an AI program that tailors training materials for officers and helps them make more accurate decisions. In other words — decision-support and training, not autonomous adjudication.
It sounds unambiguous — AI helps a human, not replace them. But the problem is that “helps” ≠ “doesn’t influence”. And this is best explained by a former Immigration Judge who saw the system from above.
Automation bias — why “AI only helps” does not mean “AI doesn’t influence”
Automation bias is a documented cognitive effect: humans trust algorithmic recommendations even when there are reasons to doubt them. If a system flagged a recommendation letter as “weak” or highlighted an inconsistency — the officer is likely to follow that signal, even if formally the human makes the decision. That is why FOIA suits seek not the final decisions but what the AI showed the officer on the screen.
“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.”
AI affects which cases are reviewed first, which issues are highlighted, how evidence is grouped. In cognitive science this is called “shaping the decision environment”. Source: BMD Law.
This is the key mechanism. AI formally does not make the decision, but it creates the information environment in which a human decides — and that environment is biased. Three independent former immigration system employees (asylum officer Joshua Perez Garcia, USCIS staff Morgan Bailey, Judge Robert Ratliff) corroborate the same phenomenon from three hierarchical levels. A detailed analysis of their testimonies is in the spoke article about AI systems.
How USCIS AI affects EB-1A, O-1 and NIW petitions
It’s important not to conflate roles. Claude 3.7 Sonnet (DHS-2598) only extracts data from the uploaded PDF during online filing — that’s intake, not substantive petition analysis. Azure AI Translator (DHS-2305) translates foreign documents. ELIS Evidence Classifier (DHS-16) tags pages by document type. These are different systems and different vendors. DHS classified the Claude PDF Intake as High-Impact = No. Claude does not classify your evidence nor write RFEs — other systems and humans do that.
If a classifier does the first-pass, a clear structure, explicit exhibit tabs and direct mapping of evidence to criteria (for EB-1A — to the 10 regulatory criteria, for NIW — to the 3 prongs of Dhanasar) give models less chance to “miss” and fail to surface the needed document to the officer.
If a letter lacks concrete factual claims (dates, numbers, projects, comparisons), it reads as boilerplate — for AI and for an overloaded officer alike. Template letters like “please write how you know them” in 2026 are riskier than in 2022.
If an officer requests additional documents on a specific criterion — read the RFE as a requirements list and respond precisely: specific facts, numbers, references to the Exhibit and page of the original petition. Petitioners who do this regularly receive approvals after an RFE.
Navigation to the three related articles
I split details into three separate publications — it’s easier to search and read. Each answers its own specific search query.
Detailed breakdown of use cases from the DHS Inventory: Claude PDF Intake, Azure Translator, ELIS Evidence Classifier, ATLAS (PIA-084), PAiTH Legal Persona, DHSChat. Vendor, model and verbatim quotes from the inventory, plus testimonies from three former insiders.
Pangea v. USCIS, Refugees International v. USCIS, Mukherji v. Miller. Who’s the plaintiff, what they seek, what’s been disclosed, actual production numbers. The impact of Loper Bright on AI adjudication litigation.
Analysis by Cozen O'Connor, Reddy Neumann Brown, The Seltzer Firm plus real cases from Reddit r/eb_1a and r/EB2_NIW. What most often “breaks” the model in EB-1A and NIW petitions. A practical checklist for the petitioner.
Disclaimer and sources
This is not legal advice. I am not a licensed immigration attorney. I collected and verified public DHS documents, court dockets and official statements — but decisions about your EB-1A, O-1 or NIW petition should be discussed with a licensed immigration attorney who knows your case. If a link in the article returns 404 — report it in the comments and I will update.
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✓DHS AI Use Case Inventory (USCIS)
dhs.gov/ai/use-case-inventory/uscis — 29 USCIS AI use cases, update January 28, 2026.
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✓Pangea Legal Services v. USCIS
Just Futures Law DHS AI FOIA — docket 1:24-cv-02809-ACR.
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✓Q3 FY2025 approval rates
Manifest Law / Boundless FY2025 — EB-1A 66.6%, NIW 54%, O-1 93.8%.
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✓Cozen O'Connor immigration alert
“Growing Use of AI in Immigration Adjudications”, April 2026.
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✓Loper Bright Enterprises v. Raimondo (2024)
SCOTUS opinion 22-451 — elimination of Chevron deference.
Related forum articles
AOS-memo: USCIS memorandum on Adjustment of Status 2026 — what changed in policy alongside AI
Spoke 1: AI systems at USCIS — detailed breakdown of use cases from the DHS Inventory
Spoke 2: FOIA suits and courts — Pangea, Mukherji, Loper Bright
Spoke 3: 4 patterns of AI-RFE and checklist — what most often “breaks” the model
Author: Egor Akimov, eliteskillset.com. Published 2026-06-02, updated per original analysis May 25, 2026.