Keywords
Credence Goods
Public Goods
Abstract
The integration of artificial intelligence into economic life has created a distinctive economic challenge that existing market and regulatory frameworks are poorly equipped to address: the systematic underproduction of calibrated AI trust. Artificial intelligence systems, particularly large language models, function as credence goods—products whose quality consumers cannot evaluate even after consumption—creating information asymmetries that prevent markets from efficiently pricing AI reliability. The calibration of AI systems—the accurate representation of their own knowledge boundaries—constitutes a public good that is structurally underproduced because individual AI developers cannot capture the social returns on calibration investment. The auditing infrastructure necessary to verify AI reliability faces compounding market failures that the Verification Tax analysis reveals are inherent rather than incidental. Drawing on three foundational references and twelve supplementary citations spanning the economics of trust, credence goods markets, public goods theory, institutional economics, and AI regulation, this paper develops a comprehensive economic framework for understanding and addressing the AI trust deficit. The analysis reveals that the MIRROR benchmark's documentation of systematic AI miscalibration, the Verification Tax's exposure of structural auditing constraints, and the practical demand for explainability in human resource analytics collectively illuminate an economic phenomenon: the AI trust market is failing in ways that require coordinated institutional intervention. The paper concludes by proposing an AI Trust Infrastructure Framework that combines market mechanisms, regulatory standards, and institutional innovations to address the fundamental economic dimensions of the AI trust challenge.
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