Year: 2023
Type: conference-paper
Abstract: Prior research has investigated children’s perceptions of algorithmic bias, but provides little guidance on engaging children in conversations on algorithmic bias that center their agency and well-being. To address this, we developed discussions and design activities based on three scenarios of algorithmic (un)fairness. We conducted these discussions and activities with 16 children (ages 8-12) in the US, and examined our data using qualitative thematic analysis. Grounded in lived experiences and situated knowledge, participants were capable of reasoning around both explicit and implicit effects of algorithmic bias. Participants also expressed distrust of technology, doubting technology’s abilities and preferring human approaches to resolve unfairness. This work contributes (1) a more nuanced understanding of children’s situated reasoning of technology, suggesting their potential for critical engagement and (2) a blueprint for engaging children in scaffolded yet open-ended sensemaking around algorithmic fairness, informing the design of tools, curricula, and other learning experiences for children.
Institution: University of Washington
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Funder: National Science Foundation
Awards: FMitF: Track I: End-User Programming with Synthesis-Guided Interaction Models, Developing Authentic and Fair Computer Science Assessments, BPC-AE: ACCESSCOMPUTING - THIRD EXTENSION, BPC-AE: AccessComputing Fourth Extension, Computing Innovation Fellows Project 2021, BPC-AE: Equity in Computing Education Policies, Pathways, and Practices (ECEP 3), Justice-Focused Secondary CS Teacher Education, FMitF: Formal Verification of Accessibility, SHF: Medium: Collaborative Research: Programming Strategies
OpenAlex ID: W4380479537