Evaluability in Artificial Intelligence-Mediated Information: Preserving the Capacity for Independent Assessment

Abstract

Artificial intelligence increasingly mediates the information through which people form and assess consequential judgments. Existing concepts including transparency, explainability, provenance, auditability, accountability, contestability, and human oversight address important dimensions of this environment, but their presence does not necessarily determine whether an independent evaluator can practically reconstruct and assess the informational foundations of a particular claim or decision. This paper develops evaluability as a relational capacity that varies by degree and centers on independent reconstruction and assessment. It distinguishes evaluability from neighboring concepts, identifies accessibility, reconstructability, source continuity, contextual preservation, comparability, and practical cost as relevant dimensions, and establishes conditions under which the concept would fail to add analytical value. The analysis then examines successive AI-mediated transformation, showing how selection, ranking, compression, summarization, and synthesis can weaken, preserve, or strengthen evaluability without producing inaccurate outputs. A clinical decision-support comparison illustrates how systems can remain accurate, transparent, explainable, auditable, and subject to meaningful human oversight while providing different practical pathways from recommendations to underlying evidence. Evaluability therefore identifies a limited but consequential feature of AI-mediated informational environments: whether they preserve practicable pathways through which appropriately situated actors can independently reconstruct and assess consequential claims.