The Black Box Presidency

66 Pages Posted: 27 Feb 2025 Last revised: 11 Mar 2025

See all articles by Andrew Chin

Andrew Chin

University of North Carolina School of Law

Date Written: February 27, 2025

Abstract

In February 2025, as wildfires ravaged Los Angeles, President Donald Trump threatened to withhold FEMA assistance unless California adopted voter ID laws and water deregulation policies-just one example of how executive power could weaponize administrative authority for political gain. Simultaneously, Elon Musk's Department of Government Efficiency (DOGE) deployed artificial intelligence systems across multiple agencies to evaluate federal workers' job justifications, with the stated goal of replacing "the human workforce with machines." This article explores how these converging developments-the politicization of administrative functions and the algorithmic replacement of civil servants-foreshadow a constitutional crisis through the Strategic AI Governance Engine (SAGE), a hypothetical yet plausible system that would automate statutory interpretation and policy implementation across federal agencies. While no unified system like SAGE currently exists, the Biden administration disclosed over 2,000 siloed AI applications across the federal government, from regulatory enforcement targeting to benefits eligibility determinations. These existing deployments, combined with DOGE's aggressive workforce reduction-over 40,000 federal employees have already accepted resignation offers-create the foundation for algorithmic governance at unprecedented scale. When paired with the Supreme Court's dismantling of Chevron deference in Loper Bright Enterprises v. Raimondo (2023) and its embrace of unitary executive theory in Seila Law LLC v. CFPB (2020), these developments create the perfect constitutional storm: a presidency empowered to centralize administrative authority through algorithmic systems that operate at "machine speed," beyond meaningful congressional oversight or judicial review. The constitutional implications are profound. SAGE's reinforcement learning algorithms could optimize for presidential priorities rather than statutory mandates across numerous domains—from environmental protection to immigration enforcement to healthcare access.

Suggested Citation

Chin, Andrew, The Black Box Presidency (February 27, 2025). UNC Legal Studies Research Paper No. 5158692, Available at SSRN: https://ssrn.com/abstract=5158692 or http://dx.doi.org/10.2139/ssrn.5158692

Andrew Chin (Contact Author)

University of North Carolina School of Law ( email )

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