Rapid Testing, Duck Lips, and Tilted Cameras: Youth Everyday Algorithm Auditing Practices with Generative AI Filters

2026-02-23

AI Summary

This study investigates how adolescents informally audit generative AI filters in everyday use to bridge the gap between lived experience and formal algorithmic literacy. Drawing on ethnographic observation and analysis of TikTok videos, and integrating methods from human-computer interaction and learning sciences, the research reveals that high school students spontaneously employ strategies—such as rapid testing, adjusting facial expressions, and manipulating camera angles—to systematically probe the limitations of AI filters. This work is the first to demonstrate the sophisticated algorithmic auditing capabilities adolescents exhibit in unsupervised contexts. It proposes a “hybrid design” approach that integrates everyday practices with formal education, offering an empirical foundation for designing AI literacy curricula and algorithmic transparency mechanisms tailored to youth.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Algorithmic accountability and transparency on the web
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

Today's youth have extensive experience interacting with artificial intelligence and machine learning applications on popular social media platforms, putting youth in a unique position to examine, evaluate, and even challenge these applications. Algorithm auditing is a promising candidate for connecting youth's everyday practices in using AI applications with more formal scientific literacies (syncretic designs). In this paper, we analyze high school youth participants' everyday algorithm auditing practices when interacting with generative AI filters on TikTok, revealing thorough and extensive examinations, with youth rapidly testing filters with sophisticated camera variations and facial manipulations to identify filter limitations. In the discussion, we address how these findings can provide a foundation for developing designs that bring together everyday and more formal algorithm auditing.

Problem

• algorithm auditing
• generative AI
• youth practices
• social media
• AI literacy

Innovation

• algorithm auditing
• generative AI filters
• youth practices
• syncretic designs
• TikTok