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

TL;DR

This paper introduces a five-step framework to help high school youth systematically and critically evaluate machine learning systems through algorithm auditing, supported by a case study with teenagers analyzing TikTok filters.

Contribution

It conceptualizes a five-step scaffolding process for teaching algorithm auditing to young learners and demonstrates its application through a practical pilot study.

Findings

Youth engaged effectively with each auditing step.

Scaffolding supported critical understanding of AI systems.

Potential for integrating auditing into classroom activities.

Abstract

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 review how expert researchers conduct algorithm audits and how end users engage in auditing practices to propose five steps that, when incorporated into learning activities, can support young people in auditing algorithms. We present a case study of a team of teenagers engaging with each step during an out-of-school workshop in which they audited peer-designed generative AI TikTok filters. We discuss the kind of scaffolds we provided to support youth in algorithm auditing and directions and challenges for integrating algorithm auditing into classroom activities. This paper contributes: (a) a conceptualization of five steps to scaffold algorithm auditing learning activities, and (b) examples of how youth engaged with each step during our pilot study.