AI-Scored Society(AISS) : A Conceptual Definition of AI Mediated Visibility Allocation

Algorithmic systems increasingly participate in the evaluation, ranking, and recommendationof digital information across search engines, social media platforms, andrecommendation infrastructures. As these computational processes become embeddedwithin digital environments, the visibility of information is frequently mediatedthrough algorithmic scoring mechanisms that prioritize, filter, and distribute contentwithin digital interfaces.This paper introduces the concept of the AI-Scored Society (AISS) to describe astructural condition in which AI-generated scoring systems mediate the allocation ofvisibility within digitally mediated environments. The concept identifies environmentsin which ranking scores, recommendation weights, relevance estimates, and relatedevaluative signals influence how information becomes visible, discoverable, and prioritizedwithin digital systems.The objective of this paper is not to propose a theoretical model or predictiveframework, but to provide a conceptual definition that clarifies the structural characteristicsof environments shaped by algorithmic visibility allocation. By defining theconcept of AI-Scored Society, the paper contributes terminological clarity for analyzingdigital environments in which algorithmic evaluation infrastructures participate in theorganization of information visibility.