Developing Machine Learning Agency Among Youth: Investigating Youth Critical Use, Examination, and Production of Machine Learning Applications

Abstract. Young people are surrounded by machine learning (ML) devices and their lived experiences are increasingly shaped by the ML technologies that are ever-present in their lives. As innovations in machine learning technologies continue to shape society, it raises important implications for what young people learn, their career trajectories, and the required literacies they need to thrive in this changing occupational environment. Youth are particularly vulnerable to the impact of ML and very little has been done to empower them to critically engage in the discourse surrounding the next generation of technologies that have a marked potential to shape their lives for better or worse. My dissertation work seeks to develop youth autonomy and agency around ML by designing an intervention that supports youth critical use, examination, and production of ML applications in the context of promoting self-expression and social good. I will conduct a qualitative single case study research and collect multiple forms of data using interviews, story completions, digital artifacts, observations, and focus group discussions. These data sources will allow me to conduct an intensive analysis and investigation of how youth populations can be supported to develop the skills, practices and critical consciousness needed to effectively engage with machine learning technologies. Through my research, I also hope to advance the literature on how young people creatively collaborate with ML and use ML for self-expression.