The Social Cost of Artificial Knowledge

This Article advances a novel conceptualization of artificial knowledge within the framework of information economics, positioning it as a distinct epistemic category that bridges tacit and explicit knowledge. Artificial knowledge systems automate human intuition (tacit knowledge) while scaling formalized reasoning (explicit knowledge), transforming de-cision-making, governance, and economic structures in ways that chal-lenge existing regulatory paradigms. Capturing artificial knowledge’s ability to integrate tacit and explicit elements underpins the Article’s core contribution: the introduction of a typology of artificial knowledge, valid-explainable, pre-valid explainable, valid-opaque, and non-valid opaque, that elucidates the trade-offs be-tween reliability and transparency in artificial systems. Building on foun-dational insights from information economics, the Article critiques the current over-emphasis on explainability in artificial knowledge govern-ance. It instead advocates for validation as a scalable alternative where explainability is impractical. Validation is operationalized in three legal dimensions: empirical, which ensures statistical accuracy; normative, which aligns outputs with societal and ethical norms; and functional, which guarantees context-specific performance. The Article develops a framework for internalizing the societal costs of artificial knowledge systems, proposing actionable reforms such as val-idation-based incentives, tiered liability regimes, and harmonized global standards. By aligning innovation with accountability, this Article aims to mitigate the externalities of artificial knowledge while preserving its trans-formative potential.