The “Cultural Filtering” of AI Guide: Algorithmic Bias and Resistance in Museum Education Space

Abstract

While AI-guided museum systems are revolutionizing educational experiences, they also pose risks of “cultural filtering.” This paper examines how AI systematically creates and reinforces cultural inequalities through multiple bias mechanisms at the data, algorithm, and application levels. Moving beyond criticism, it proposes a multi-stakeholder resistance framework encompassing algorithmic auditing, interdisciplinary interventions, open-source tools, data rights, and critical literacy cultivation across institutional, technological, and public participation dimensions. The study advocates establishing museum algorithmic ethics standards centered on transparency, inclusivity, cultural sensitivity, and public interest, ensuring technology serves cultural understanding rather than perpetuating biases. This framework provides actionable guidance for building equitable digital-era museum education spaces.