AI-Mediated Entity Familiarity: repeated AI surfacing and its cognitive consequences
Antonio Catalano · Zenodo (CERN European Organization for Nuclear Research) · 2026
Algorithmic Familiarity: AI-Mediated Entity Familiarity and Its Cognitive Consequences is a conceptual working paper that introduces and defines Algorithmic Familiarity, more precisely termed AI-mediated entity familiarity. The construct refers to the possibility that repeated and contextually consistent exposure to brands, products, concepts, institutions, tools, or other entities within AI-generated outputs may increase their cognitive accessibility and likelihood of entering users’ consideration sets. The paper positions the construct in relation to adjacent literatures on mere exposure, processing fluency, availability, trust in automation, cognitive offloading, chatbot familiarity, algorithmic advice, algorithmic bias, and AI availability. It argues that Algorithmic Familiarity concerns a downstream cognitive process: not whether an AI system retrieves an entity, but what repeated AI-mediated exposure may do to users’ perceptions of that entity. The paper develops a conceptual model, proposes testable propositions, and introduces a measurement architecture comprising the AI Awareness Index (AAI), AI Familiarity Index (AFI), AI Consideration Index (ACI), and AI Consideration Score (ACS). These measures are explicitly framed as system-level indicators of AI-mediated exposure rather than direct measures of user-level familiarity, preference, or choice. Establishing such cognitive effects requires controlled user-level experiments or longitudinal observation. This version is a conceptual working paper and has not been peer-reviewed.