Hooverism as a Framework for Understanding the Historical and Unethical Nature of the Use of Artificial Intelligence and Other Surveillance Practices in the United States
Hooverism as a Framework for Understanding the Historical and Unethical Nature of the Use of Artificial Intelligence and Other Surveillance Practices in the United States

Summary/Abstract: This theoretical paper establishes Hooverism as a framework for analyzing the ideological continuities between J. Edgar Hoover's 48 year tenure as FBI director (1924-1972) and contemporary algorithmic surveillance systems. Hooverism is defined by four interconnected pillars of informational supremacy, loyalty enforcement, racialized control, and bureaucratic secrecy that structured mid-20th century domestic intelligence and now shape modern AI governance. Drawing on surveillance studies, critical data theory, and institutional history, I demonstrate that contemporary biometric surveillance, predictive policing, and algorithmic risk assessment automate and scale Hooverist logic rather than transcending it. Through systematic analysis of historical precedents and modern developments in algorithmic governance, I trace how informational supremacy manifests as mass data collection justified by security claims; loyalty enforcement operates through automated flagging of "suspicious" behavior without criminal predicates; racialized control persists through biased training data and discriminatory deployment; and bureaucratic secrecy functions through vendor nondisclosure agreements (NDA) and algorithmic opacity. This pattern reveals that surveillance ideology precedes and transcends specific technologies. AI amplifies existing institutional practices rather than creating new forms of bias. The framework demonstrates that legislative reforms addressing individual technologies prove inadequate because they fail to disrupt underlying ideological structures. I provide the Hooverism framework, which establishes six governance principles: equity, ethics, transparency, stakeholder engagement, oversight, and democratic accountability, with operational protocols for bias auditing, community consent mechanisms, and sunset provisions. This framework enables policymakers and scholars to identify when algorithmic systems automate historical injustice rather than technical error, supporting interventions that address root causes rather than symptoms. By exposing how surveillance becomes normalized through the language of security and administrative neutrality, this analysis provides conceptual tools for resisting governance through suspicion and establishing democratic accountability in the era of algorithmic governance.

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