Law Office of Hong-min Jun · Chicago · Indianapolis
Computer Science Researcher
Machine Learning & Algorithmic Fairness · South Korea
D. Kim is a Ph.D. graduate from Indiana University specializing in algorithmic fairness and bias mitigation in machine learning systems. His research developed a framework for detecting and correcting racial and gender bias in automated hiring, credit scoring, and criminal justice risk assessment algorithms — tools now used by the EEOC in AI audit frameworks. He also contributed foundational work on privacy-preserving federated learning.
AI/ML NIW cases are increasingly common and require strong differentiation. We needed to demonstrate that D. Kim's work had policy-level impact on civil rights and national governance — not just technical innovation in a competitive commercial field.
D. Kim's bias detection framework has been adopted by the EEOC as part of its AI hiring audit guidelines, protecting an estimated 80M workers subject to algorithmic employment screening annually. His federated learning privacy framework was cited in the FTC's AI commercial surveillance policy guidelines.
The Executive Order on Safe, Secure, and Trustworthy AI (2023) explicitly mandates algorithmic fairness testing across federal procurement systems. D. Kim's framework is cited in NIST's AI Risk Management Framework and has been adopted by three federal agencies as their compliance testing tool.
His algorithmic fairness work involves sensitive civil rights data shared with EEOC under privacy agreements. The PERM process would require interrupting these confidential collaborations. Additionally, his ongoing DARPA AI Exploration grant ($600K) cannot be transferred to a new researcher without a 12-month restart penalty.
AI ethics and fairness is one of the strongest emerging NIW categories because it intersects directly with federal civil rights mandates. The EEOC adoption was the cornerstone — it is difficult for USCIS to argue that work explicitly adopted by a federal civil rights enforcement agency lacks national importance. The multi-agency citation pattern (EEOC, NIST, FTC) created a powerful and diversified national importance narrative that was nearly impossible to rebut.
USCIS Approval Notice
Client identity redacted for confidentiality
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