Comment from Christopher Huynh

I am a doctoral candidate in Industrial-Organizational Psychology at Virginia Tech whose research focuses on artificial intelligence, machine learning, psychometrics, workplace assessment, organizational learning, and the future of work. My research examines how AI can improve employee assessment, reduce algorithmic bias, enhance learning outcomes, and help organizations make more effective and equitable decisions. I strongly oppose the proposed revisions contained in OMB-2026-0034 because they would undermine the scientific foundations that make this work possible. Section 200.205 is particularly troubling because it would allow political appointees to override expert peer review when evaluating grant proposals. As a researcher working at the intersection of psychology, artificial intelligence, and organizational science, I depend on funding decisions being made by qualified experts capable of evaluating technical methodology, psychometric rigor, and scientific merit. Peer review is not merely an administrative step; it is a core safeguard that ensures federal research funding is allocated based on evidence and expertise rather than political priorities. Allowing political intervention in this process threatens the credibility of federally funded science and risks discouraging innovative research in emerging fields. I am also deeply concerned about Section 200.340, which would permit discretionary termination of grants at any time. My research includes multi-year projects on generative AI in education, AI-assisted measurement development, organizational assessment, and algorithmic bias mitigation. These projects require extensive planning, data collection, collaboration, and analysis. If funding can be withdrawn after a project has already begun, researchers may be unable to complete studies, analyze data, publish findings, or fulfill commitments made to participants and partner institutions. Such uncertainty would discourage ambitious research, waste taxpayer investments, and slow scientific progress. Section 200.220 would further harm my work by restricting international collaboration. Many of the questions surrounding artificial intelligence, workforce transformation, and assessment fairness are global challenges that require global evidence. My research often seeks to understand whether AI-based tools and psychological measures function consistently across different populations and contexts. International collaborators provide access to diverse samples, specialized expertise, and opportunities to test the generalizability of findings. Restricting these partnerships would reduce the quality of scientific evidence and weaken the United States' position as a leader in AI and behavioral science research. Finally, Sections 200.432, 200.454, and 200.461 would restrict conference attendance, subscriptions, and publication-related costs. These activities are not optional expenses. Conferences allow researchers to receive critical feedback, build collaborations, and rapidly disseminate new findings. Publication and open-access costs ensure that federally funded research is accessible to practitioners, policymakers, educators, and other researchers. Limiting these activities would reduce the impact and public value of taxpayer-funded science. I respectfully urge OMB to withdraw these provisions and refrain from implementing the proposed rule. The changes outlined in OMB-2026-0034 would weaken peer review, increase political influence over scientific funding, create instability in federally funded research, restrict international collaboration, and reduce the dissemination of knowledge. Collectively, these changes threaten the integrity, independence, and global competitiveness of American science. CV in attached file