Efficiency at What Cost? Partnerships and AI-Driven AI Reforms in the U.S. and China

• In Event: Governing AI: Framing, Adoption, and Accountability in the Public Sector

Abstract

Abstract
This study examines how partnerships among policymakers, private-sector innovators, and civil society groups shape AI-driven governmental reforms in the United States and China, with an emphasis on delivering transformative and resilient policy solutions. By focusing on Elon Musk’s Department of Government Efficiency (DOGE) in the U.S. and the systematic integration of AI in China’s public administration, the research highlights both the promise and pitfalls of collaboration in advancing efficient and equitable governance.

Research Question
The central inquiry explores how collaborative initiatives—spanning public agencies, technology firms, and policy stakeholders—influence AI-based reforms aimed at enhancing administrative efficiency. The study asks whether these partnerships foster truly inclusive, resilient policy outcomes or merely serve as conduits for concentrated decision-making power under the banner of modernization.

Theoretical Framework
Drawing on frameworks of collaborative governance and institutional resilience, this research underscores the interplay between cross-sector partnerships, policy design, and public trust. Collaborative governance theory posits that involvement from diverse actors can generate more robust policy solutions by integrating multiple perspectives and spreading risk. Meanwhile, institutional resilience theories suggest that successful reform depends on balanced power dynamics and transparent decision-making processes that allow for adaptation when unintended consequences arise.

Methodology
A comparative case study method is employed, with data gathered from policy documents, legislative records, media analyses, and expert interviews in both countries. In the U.S. case, attention is directed toward DOGE’s efforts to partner with technology innovators, evaluate cost-cutting measures, and encourage citizen feedback. In China, the research investigates how government agencies collaborate with AI firms and policy experts to pilot systems like DeepSeek, ensuring consistent standards for data management while seeking to maintain public trust. Collectively, these sources provide insights into how collaborative arrangements are structured, negotiated, and evaluated in different political and administrative contexts.

Key Findings
Preliminary findings reveal that the U.S. model—characterized by swift policy shifts under Musk’s leadership—benefits from private-sector ingenuity but has faced criticisms regarding accountability and inclusiveness. Stakeholders express concerns that rapid workforce realignment and agency consolidations may erode institutional knowledge and sideline public voices. In China, the state-directed approach leverages strategic partnerships with AI developers, enabling coordinated rollouts of new technologies that demonstrate efficiency gains, particularly in processing government services and documents. However, questions remain about transparency, data security, and the extent to which these partnerships allow independent oversight.

By examining these two cases side-by-side, the study argues that forging effective collaborations across government tiers, private industries, and civic organizations can bolster resilience and policy innovation—but only if transparency, stakeholder engagement, and legal safeguards are prioritized. When partnerships privilege narrow interests or obscure decision-making processes, AI-driven reforms risk amplifying inequities rather than delivering transformative, equitable solutions.

In conclusion, this research highlights the pivotal role of collaboration in shaping AI-based policy reforms and underscores the conditions under which such partnerships can yield inclusive, accountable, and enduring outcomes. By illuminating how diverse stakeholders engage with technological innovations, the findings contribute to broader discussions on designing and implementing evidence-based policies that uphold public trust and enhance governmental efficacy.

Author

• Chao Song, University of Chinese Academy of Social Sciences
  Presenting Author