Recognition without obligation: AI, asset position, and the limits of structural explanations

This study examines whether structural explanations shape domains of political evaluation and whether their effects vary by informational source and asset position. Using a survey experiment, it distinguishes among structural attribution, public responsibility, and policy support. Structural explanations shift causal attribution under both human and AI sources, but source-dependent differences emerge for responsibility and policy support. These source-dependent differences vary by asset position and are largest for policies with explicit fiscal implications. The findings show that the political effects of structural explanations depend on the evaluation being made, the source to which the explanation is attributed, and respondents’ material position.