EB-2 NIW · Profession Guide
EB-2 NIW for AI and ML researchers: AAO Data, Denial Patterns & Evidence
How AI/ML researcher EB-2 NIW petitions tend to fail at AAO and what evidence has historically cleared the Dhanasar three-prong test.
Based on 6,362 real USCIS AAO decisions · Last updated May 2026
Short answer
Across 389 AI / Machine Learning Researcher AAO decisions in our corpus, 8.4% were approved on appeal, 80.3% were denied, and 11.3% were remanded. The single most common denial reason for AI and ML researchers is “Commercial vs. research framing.” AAO rates are lower than first-pass USCIS rates because these cases were already denied at least once.
AAO outcomes for AI and ML researchers (389 decisions)
Read this carefully: AAO numbers reflect petitions that were already denied at least once and appealed. First-pass USCIS approval rates are substantially higher. Use these figures to understand which arguments USCIS finds insufficient at the highest scrutiny level.
Why AI and ML researchers get denied at AAO
Most common AAO denial reason in this bucket:
Commercial vs. research framing
AAO frequently rejects AI/ML researcher petitions where the work product is published only as company blog posts, internal tech reports, or product demos. When the same researcher has refereed publications, citations from independent groups, and contributions to public benchmarks, the same underlying work is treated very differently — but the petitioner has to surface those artifacts explicitly.
What strong AI/ML researcher petitions tend to include
These are the evidence types that recur in approved AI / Machine Learning Researcher cases. Not every approved petition has all of them, but petitions missing several typically struggle at AAO.
- 1Refereed publications at top-tier venues (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR) with acceptance-rate evidence
- 2Independent citation counts (Google Scholar, Semantic Scholar) — h-index and i10-index help anchor scale
- 3Open-source artifacts: HuggingFace model downloads, GitHub star counts, PyPI install metrics
- 4Standard-setting or benchmark contributions (MLPerf submissions, BIG-bench tasks, reproducibility report co-authorship)
- 5Invited talks at academic seminars / workshops (not vendor-sponsored panels)
- 6Independent expert letters from researchers at universities or labs you have not collaborated with
How AI/ML researcher cases fit the Dhanasar three-prong test
The Dhanasar framework asks USCIS to evaluate three things together: substantive merit, your positioning to advance the work, and whether waiving the labor cert makes sense on balance. Here is how the prongs typically frame for AI and ML researchers.
Prong 1 — Substantive merit and national importance
Map your work to a national-priority area: AI safety, healthcare ML, defense ML, semiconductor / chip design ML, climate modelling.
Prong 2 — Well-positioned to advance the proposed endeavor
Citations + downloads + invited talks together establish that you, specifically, are well-positioned to advance the field.
Prong 3 — On balance, waiver is in the national interest
Argue waiver because labor cert delay would freeze ongoing research collaborations — quantify with grant cycles.
What approved AI / Machine Learning Researcher profiles look like
~5+ first/second-author publications at refereed venues, h-index in the double digits for the field, and at least one widely-used open-source artifact.
This is a composite based on patterns across 389 AAO decisions — not any single case. Your specific profile may clear with less, or struggle with more, depending on framing.
Run a personalized AI / Machine Learning Researcher case analysis
Aggregate data tells you what AAO has rejected for AI and ML researchers. A $10 ai case review tells you which of those failure modes your profile is closest to — prong by prong, with the five most-similar AAO cases pulled directly from the same 6,362-decision corpus.
One-time payment, no subscription. Greenway AI is a data + document-generation platform, not a law firm; nothing here is legal advice.