Learning about Data, Algorithms, and Algorithmic Justice on TikTok in Personally Meaningful Ways

Luis Morales-Navarro; Yasmin B. Kafai; Ha Nguyen; Kayla DesPortes; Ralph Vacca; Camillia Matuk; Megan Silander; Anna Amato; Peter Woods; Francisco Castro; Mia Shaw; Selin Akgun; Christine Greenhow; Antero Garcia

arXiv · 2405.15437

Learning about Data, Algorithms, and Algorithmic Justice on TikTok in Personally Meaningful Ways

Luis Morales-Navarro·Yasmin B. Kafai·Ha Nguyen·Kayla DesPortes·Ralph Vacca·Camillia Matuk·Megan Silander·Anna Amato·Peter Woods·Francisco Castro·Mia Shaw·Selin Akgun·Christine Greenhow·Antero Garcia

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Abstract

TikTok, a popular short video sharing application, emerged as the dominant social media platform for young people, with a pronounced influence on how young women and people of color interact online. The application has become a global space for youth to connect with each other, offering not only entertainment but also opportunities to engage with artificial intelligence/machine learning (AI/ML)-driven recommendations and create content using AI/M-powered tools, such as generative AI filters. This provides opportunities for youth to explore and question the inner workings of these systems, their implications, and even use them to advocate for causes they are passionate about. We present different perspectives on how youth may learn in personally meaningful ways when engaging with TikTok. We discuss how youth investigate how TikTok works (considering data and algorithms), take into account issues of ethics and algorithmic justice and use their understanding of the platform to advocate for change.

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Luis Morales-Navarro, Yasmin B. Kafai, Ha Nguyen, Kayla DesPortes, Ralph Vacca, Camillia Matuk, Megan Silander, Anna Amato, Peter Woods, Francisco Castro, Mia Shaw, Selin Akgun, Christine Greenhow, Antero Garcia. 2024-05-24. Learning about Data, Algorithms, and Algorithmic Justice on TikTok in Personally Meaningful Ways. https://arxiv.org/abs/2405.15437

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