Regulating AI Displacement: Public Demand, Institutional Credibility, and the Politics of Employment Protection in Latin America

How citizens want artificial intelligence (AI) regulated will shape which AI governance instruments are politically feasible. Yet we know little about public preferences over AI policy in middle-income democracies, where state capacity is contested. This paper provides the first multi-country experimental evidence on preferences over AI labordisplacement policy instruments in middle-income democracies. Using a randomized framing experiment in a sur-vey of Argentina, Brazil, Colombia, and Mexico, we examine how citizens map beliefs about AI-driven job disruption into demand for three governance responses: worker-retention reg-ulation, tax-financed redistribution to displaced workers, or non-intervention. Nearly three-quarters of respondents prefer employment-preserving regulation, about one in twenty favors tax-financed redistribution, and one-fifth prefer nonintervention. A three-arm framing taxonomy spanning alarmist, delayed-threat, and task reallocation narratives produces no average shift in this distribution, and the heterogeneous effects we detect at the margin run from non-intervention toward regulation, not toward redistribution. The pattern is consistent with preferences anchored in priors about the state's redistributive capacity rather than in correctable beliefs about AI, with direct implications for AI governance design in low-credibility institutional environments. A within-survey wording experiment on employment protection regulation elicits a substantial shift in preferences among the same respondents, indicating that the null on the AI framings is not an artifact of respondent inattention.