Learning About Algorithm Auditing in Five Steps: Scaffolding How High School Youth Can Systematically and Critically Evaluate Machine Learning Applications

This paper presents a five-step framework for teaching high school students to systematically audit machine learning systems through hands-on activities, demonstrated via a case study where teens audited peer-designed TikTok filters to evaluate their limitations and biases. The study focuses on scaffolding critical evaluation skills and algorithmic accountability rather than evaluating AI tutoring systems or education-specific AI tools.

While there is widespread interest in supporting young people to critically evaluate machine learning-powered systems, there is little research on how we can support them in inquiring about how these systems work and what their limitations and implications may be. Outside of K-12 education, an effective strategy in evaluating black-boxed systems is algorithm auditing—a method for understanding algorithmic systems’ opaque inner workings and external impacts from the outside in. In this paper, we

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