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"Would You Rather Have it be Accurate or Diverse?" How Male Middle-School Students Make Sense of Algorithm Bias

As data-driven decisions become more ubiquitous, it will be critical for youth to understand the impacts of algorithm bias. In this study, we discuss the design of an extracurricular data science program and examined how the participants (12 males, ages 11 13) made sense of algorithm bias and discrimination. We conducted a critical discourse analysis on one classroom discussion. Results suggest that participants showed initial understandings that algorithms contain biases that may perpetuate discrimination. Introduction and theory Big data algorithms are becoming increasingly relevant in social and policy decisions and can now be used to decide whether people secure employment, receive loans, or are convicted of a crime. Unfortunately, such algorithms reinforce racial and gender discrimination at large scales and are largely unintelligible to the public (Noble, 2018; O’Neil, 2016). As these big data-driven decisions become more ubiquitous, it will be critical for young people to understand computational statistics and the ethical consequences of algorithm bias. Recent studies have examined big data practices for middle-school students (Jiang & Kahn, 2019) and the intersections of data visualizations and spatial justice (Rubel et al., 2017). However, few have investigated how young people engage discursively with big data algorithm bias and racial/gender discrimination. In this study, we designed a data science extra-curricular course for middle school students. The research question is How do male middle-school students engage in algorithm bias and discrimination topics in a data science curriculum? Philip and colleagues (2013) have proposed a framework on big data for democratic participation that is rooted in both sociocultural and critical pedagogy theories. Using these two perspectives, Philip and colleagues proposed three categories of student objectives for democratic data science: content proficiency and discursive fluency (the ability to use language and tools of the discipline), motivated use of content (learners see themselves as users of data science and work towards greater justice and equity in society), and the politics of knowledge (learners know that data is political and address limitations/opportunities for particular populations). These three categories purposefully center topics of inequities, power, and ethics around big data algorithms.