  As artificial intelligence/machine learning (AI/ML) applications become more
pervasive in youth lives, supporting them to interact, design, and evaluate
applications is crucial. This paper positions youth as auditors of their peers'
ML-powered applications to better understand algorithmic systems' opaque inner
workings and external impacts. In a two-week workshop, 13 youth (ages 14-15)
designed and audited ML-powered applications. We analyzed pre/post clinical
interviews in which youth were presented with auditing tasks. The analyses show
that after the workshop all youth identified algorithmic biases and inferred
dataset and model design issues. Youth also discussed algorithmic justice
issues and ML model improvements. Furthermore, youth reflected that auditing
provided them new perspectives on model functionality and ideas to improve
their own models. This work contributes (1) a conceptualization of algorithm
auditing for youth; and (2) empirical evidence of the potential benefits of
auditing. We discuss potential uses of algorithm auditing in learning and
child-computer interaction research.
