Bodies in AIML, Embodied Justice: Discussing Algorithmic Harm and Justice Through Sports-Based Culturally Relevant Computing Education (Poster 2)

• In Event: Perspectives on Promoting Algorithmic Justice in Artificial Intelligence and Machine Learning Education in K–12

Abstract

Objectives: Sports can provide unique and productive entry points into discussions about the fairness and justice implications of computing in people’s everyday lives. Sports and movement offer an engaging and culturally grounded context to engage with computing and AIML in professionally and personally authentic manners. Our curriculum includes demonstrating technologies used in professional and semi professional contexts for training and sports improvement – including wearables, sensor enabled balls, and video camera based movement and body detection. Numerous surveillance implications around tracking movement (from bodies as well as objects), and video tracking through faces and bodies can be discussed from this context, and extending to discussing the everyday uses of the same technologies.
Theoretical Framework: We center learning through a constructionist lens (Papert, 1980). After playing with pre-made technologies, we introduce ways to make the same, and then ask learners to brainstorm, design, and create new sports technologies – aimed at improving their own engagement with sports, either as a practicing athlete, a relatively casual player, or even a spectator.
Methods and Data Sources: We reflect on our noticings and design iterations across a variety of implementations of this curriculum. Through school settings, summer camp programming, and events at drop-in learning spaces, we present highlights of ways conversations and concepts relating to algorithmic justice emerged directly from students and other educators, and those consciously embedded in our curriculum through reflection and design.
Results & Significance: Our 3rd grade partner teacher described how pose and face detection technologies excited students in creating movement based games. When ideating on different ways to use these technologies, students recalled its similarity to home security devices, and began discussing how they could create tools to differentiate people, and design them to address issues like home and school safety. Connecting to earlier classroom conversations around surveillance and issues of discrimination in policing practices (Lee & Chin, 2022), the teacher was able to surface a critical tension – how safety enabled through such AIML driven tools can often cause harm when not implemented with appropriate accountability, transparency, and caution around who handles the data and consequences around the same (Benjamin, 2019). When discussing movement and activity detection through wearables, we highlight how the introduction of sleep and activity trackers in professional sports has been a contentious issue gradually shifting power away from athletes and in the hands of managers and big technology companies (Taylor, 2017). Relatedly motion sensors built into phones and often accessible by all applications, most commonly used for tracking activities like running and workouts, can also be used to identify a variety of off screen activities like typing on keyboards on the same table as a phone – posing security risks worth being aware and conscious of (Yuksel et al., 2019). These highlight a novel entry point to think about algorithmic justice, and offer a provocation – how can we move outside of discussions and find more culturally relevant creation oriented activities that can support rich and conscious reflection around the issues of justice of fairness in computing and beyond.

Authors

• Vishesh Kumar, Northwestern University

• Marcelo A.B. Worsley, Northwestern University