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 scaffolding framework for teaching high school students (ages 14-15) to conduct algorithm audits of AI/ML systems, demonstrated through a workshop where youth audited peer-designed TikTok filters. The framework supports systematic critical evaluation of AI systems by having students formulate questions, design tests, collect data, analyze patterns, and communicate findings.

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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