Algorithmic Mirror: Designing an Interactive Tool to Promote Self-Reflection for YouTube Recommendations

Big Data analytics and artificial intelligence (AI) systems derive non-intuitive and often unverifiable inferences about individuals’ behaviors, preferences, and private lives. Drawing on diverse, feature-rich datasets of unpredictable value, these systems erode the intuitive connection between our actions and how we are perceived, diminishing control over our digital identities. While Explainable Artificial Intelligence (XAI) scholars have attempted to explain the inner workings of algorithms, their visualizations frequently overwhelm end-users with complexity. This research introduces ‘hypothetical inference’—a novel approach that uses Large Language Models (LLMs) to simulate how algorithms might interpret users’ digital footprints and infer personal characteristics without requiring access to proprietary platform algorithms. Through empirical studies with fourteen participants, we advance the discourse on algorithmic literacy by providing users with accessible insights into potential algorithmic interpretations of their data.