Hybrid Algorithmic Governance in U.S. Welfare Administration: State- and County-Level AI as a Case of Support-Control Convergence

Abstract

This article examines the institutional conditions under which artificial intelligence systems in U.S. welfare administration come to operate as instruments of support or as instruments of control. Rather than asking what welfare algorithms "really" are (tools of proactive assistance or infrastructures of surveillance) the article starts from the premise that support and control are co-present within the same system, while their relative balance shifts over time. This movement is conceptualized through the notion of support-control convergence and the model of an institutional ratchet. Routine budgetary and political pressures make control-oriented effects easily measurable and politically capitalizable, whereas a return toward support requires external intervention of disproportionate force, such as judicial compulsion, legislative prohibition, or public scandal. Empirically, the article draws on process tracing of six state- and county-level cases: NYSDOL fraud detection, Michigan MiDAS, Illinois Medicaid managed care, LA County homelessness prevention, the Allegheny Family Screening Tool, and Washington Foster Care. The findings show that the system's orientation is shaped by institutional design, with the decisive parameter being the side on which the costs of algorithmic error are placed. Drift toward control is routine, while reversal is exceptional and costly. In the MiDAS case, activation required a single administrative decision, whereas reversal took nine years and a $20 million settlement; even then, the system did not return to a support-oriented configuration.