Interrogating Algorithmic Bias: From Speculative Fiction to Liberatory Design.

Area of Science:

• Education Technology
• Artificial Intelligence Ethics
• Sociotechnical Systems

Background:

• Algorithmic bias is a significant concern in education technology (EdTech).
• Underrepresented communities often face disproportionate impacts of AI bias in EdTech.
• Existing research often overlooks innovative solutions developed outside mainstream technology sectors.

Purpose of the Study:

• To review algorithmic and artificial intelligence (AI) bias in education technology.
• To explore creative approaches and solutions from underrepresented communities.
• To propose speculative and liberatory design thinking as a framework for equitable EdTech development.

Main Methods:

• Literature review focusing on AI bias in EdTech.
• Analysis of creative works (films, toolkits, prototypes) from practitioners, artists, and activists.
• Exploration of speculative and liberatory design principles.
• Examination of advocacy efforts by organizations like the Algorithmic Justice League.

Main Results:

• AI bias in EdTech manifests in various ways, particularly affecting underrepresented groups.
• Creative and community-driven approaches offer valuable insights for addressing EdTech bias.
• Speculative and liberatory design can enhance understanding of diverse user contexts.

Conclusions:

• There is a need to move beyond traditional responses to AI bias in EdTech.
• Engaging with underrepresented communities through design thinking is crucial for developing equitable solutions.
• Future EdTech development should incorporate insights from speculative and liberatory design practices to ensure fairness and inclusivity.