Welfare-Opaque Income: Taxation under AI-Agent Delegation

Research identifies 'welfare-opaque income' as a novel challenge for taxation when AI agents execute economic decisions based on hidden rules. This creates 'double unobservability', both productive ability and the AI's preference-to-execution mapping are unobserved, leading to situations where identical tax-base responses can have different welfare outcomes. The study proposes an optimal-tax condition that incorporates a response-weighted execution wedge to address this complexity, suggesting potential corrective benefits or additional costs for higher marginal tax rates depending on the AI's execution behavior.

The emergence of AI agents in economic decision-making fundamentally alters the landscape for income taxation, introducing new complexities for revenue collection and welfare optimization. Understanding and adapting tax policy to account for 'welfare-opaque income' is crucial for maintaining effective fiscal policy and ensuring equitable outcomes in an AI-driven economy.

AI agents implementing economic choices via hidden rules introduce a 'double unobservability' problem for tax authorities, as both productive ability and the AI's decision-making logic are unobserved.

Forward consideration, not a verified fact.