International Society of the Learning Sciences (ISLS)
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 extra-curricular 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.
Irgens, G. A. & Thompson, J. (2020). “Would You Rather Have it be Accurate or Diverse?” How Male Middle-School Students Make Sense of Algorithm Bias. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 2 (pp. 751-752). Nashville, Tennessee: International Society of the Learning Sciences.