教高中生分五步系统审计生成式AI，培养批判性思维
Learning About Algorithm Auditing in Five Steps: Scaffolding How High School Youth Can Systematically and Critically Evaluate Machine Learning Applications
- 设计五步框架引导青少年从外部审视算法运作
- 真实案例中青少年成功审计了自研AI滤镜的局限与影响
- 适合教育者在课堂或兴趣小组中引入算法批判教学
尽管公众普遍关注支持年轻人批判性评估机器学习系统，但关于如何引导他们探究这些系统的工作原理、局限性及其潜在影响的研究仍很匮乏。在中小学教育之外，算法审计是一种有效方法，可从外部理解黑箱系统的内部机制与外部影响。本文综述专家如何开展算法审计，并分析终端用户实践，提出五个可融入学习活动的步骤，以支持青少年进行算法审计。通过一个课外工作坊案例，展示一群青少年在审计同龄人设计的生成式AI TikTok滤镜时，逐项应用这五个步骤的过程。研究讨论了为青少年提供的认知支架，以及将算法审计融入课堂教学的前景与挑战。论文贡献包括：(a) 提出一套用于指导算法审计学习活动的概念框架；(b) 展示青少年在试点研究中参与各步骤的具体实例。
原文摘要 · Abstract (English)
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.
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