Agenda Evolution and Cross-Agency Coordination Identification in U.S. Artificial Intelligence Policy during Trump's Second Term: an ACE-LDA-Based Policy Text Analysis

Authors

• Hongkun TIAN, School of Public Security Information Technology and Intelligence, Criminal Investigation Police University of China, Shenyang 110854

Keywords

Trump's second term, U.S. artificial intelligence policy, policy text mining, topic modeling, cross-agency coordination

Abstract

[Purpose/significance] In response to the frequent issuance of U.S. artificial intelligence (AI) policies, pronounced textual heterogeneity, and strengthening cross-agency linkages during the early period of Trump's second term, this study identifies the agenda structure, stage-specific shifts, and patterns of cross-agency coordination in U.S. AI policy, thereby providing a basis for science and technology security assessment in China. [Method/process] Using a corpus of 26 national-level and agency-level policy documents issued in 2025, this study constructs an ACE-LDA framework that builds on LDA document-topic distributions by incorporating cross-period topic alignment, a conservation mechanism for topic transitions, and a verifiable evidence chain. The framework is used to conduct topic identification, quarterly comparison, and agency-topic aggregation. [Result/conclusion] U.S. AI policy is structured around four themes: national-level AI action and competitiveness, agency-level AI strategies and capability building, federal AI governance and procurement rules, and agency compliance and risk management. Together, these themes exhibit a chain-like pattern of cross-agency coordination encompassing narrative framing, rule translation, use-case deployment, and implementation calibration. The policy focus gradually shifts from national mobilization toward translation into institutional arrangements, risk governance, and rule consolidation. The underlying governance logic is characterized by centralized goal setting, layered use of policy instruments, and feedback-based accountability. Particular attention should be paid to the spillover effects and associated risks arising from interactions among standards, procurement, compliance, and cross-border rules.

First Page

125

Last Page

136

Submission Date

2026-04-03

Revision Date

2026-05-09

Acceptance Date

18-May-2026

Published Date

1-Oct-2026

Reference

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Digital Object Identifier (DOI)

10.19809/j.cnki.kjqbyj.2026.04.012

Recommended Citation

TIAN, Hongkun (2026) "Agenda Evolution and Cross-Agency Coordination Identification in U.S. Artificial Intelligence Policy during Trump's Second Term: an ACE-LDA-Based Policy Text Analysis," Journal of Scientific Information Research: Vol. 8: Iss. 4, Article 12.
DOI: 10.19809/j.cnki.kjqbyj.2026.04.012
Available at: https://eng.kjqbyj.com/journal/vol8/iss4/12