Designing with and for Youth: A Participatory Design Research Approach for Critical Machine Learning Education

• Published in: Educational Technology ＆ Society Vol. 25; no. 4; pp. 126 - 141
• Main Authors: Irgens, Golnaz Arastoopour, Adisa, Ibrahim, Bailey, Cinamon, Quesada, Hazel Vega
• Format: Journal Article
• Language: English
• Published: Palmerston North International Forum of Educational Technology ＆ Society 01.10.2022; International Forum of Educational Technology & Society, National Taiwan Normal University, Taiwan; International Forum of Educational Technology & Society
• Subjects: After School Programs; algorithmic bias; Algorithms; Artificial Intelligence; Big Data; Children; Co-design; community youth program; Computer Science Education; Cooperation; critical data science; Data mining; Data science; Education; Educational Environment; Ethics; Machine learning; Mathematics; participatory design research; Program Design; Research Methodology; Researchers; Role; Special Issue Articles; Study and teaching; Young adults; United States; Participatory design research; Algorithmic bias; Critical data science; Community youth program; Machine learning
• ISSN: 1176-3647; 1436-4522; 1436-4522
• DOI: 10.30191/ETS.202210_25(4).0010

As big data algorithm usage becomes more ubiquitous, it will become critical for all young people, particularly those from historically marginalized populations, to have a deep understanding of data science that empowers them to enact change in their local communities and globally. In this study, we explore the concept of critical machine learning: integrating machine learning knowledge content with social, ethical, and political effects of algorithms. We modified an intergenerational participatory design approach known as cooperative inquiry to co-design a critical machine learning educational program with and for youth ages 9-13 in two after-school centers in the southern United States. Analyzing data from cognitive interviews, observations, and learner artifacts, we describe the roles of children and researchers as meta-design partners. Our findings suggest that cooperative inquiry and meta-design are suitable frameworks for designing critical machine learning educational environments that reflect children's interests and values. This approach may increase youth engagement around the social, ethical, and political implications of large-scale machine learning algorithm deployment.