Evaluability in AI-Mediated Systems When Verification Costs More Than Persuasion

Artificial intelligence systems increasingly reduce the costs associated with producing persuasive informational outputs while leaving the work of independent verification comparatively resource intensive. Contemporary AI governance discussions have largely focused on characteristics of informational outputs, including accuracy, transparency, explainability, fairness, and accountability. Although these concerns remain essential, they often devote comparatively less attention to whether actors outside the systems generating informational claims retain meaningful opportunities for independent inspection.

This paper introduces evaluability as a complementary governance condition referring to the practical capacity to inspect, challenge, compare, and assess informational claims. It argues that AI-mediated informational environments increasingly alter the relationship between persuasion and verification, creating conditions in which confidence in informational outputs can develop more rapidly than the practical capacities necessary to evaluate them independently. The paper examines several mechanisms contributing to this dynamic, including summarization, ranking systems, retrieval infrastructures, and generated synthesis.

The paper further explores how evaluative capacities become unevenly distributed across institutions and publics. Finally, the paper considers the implications of treating evaluability as a governance objective for sustaining accountability, contestability, public trust, and the resilience of knowledge systems within increasingly AI-mediated informational environments.