Algorithmic Bias
How adaptive systems may disadvantage certain groups — training data bias, proxy variables, and feedback loops.
Bias Source Identifier
Interactive diagnostic tool mapping the sources of algorithmic bias in educational AI systems. Explore each source to understand its type, severity, and how to detect and mitigate it. Switch to Audit Workflow mode for a step-by-step bias checking process.
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Education Bias Examples
Gallery of documented and illustrative cases of AI bias in education. Explore individual cases or switch to Pattern Analysis to see how recurring root causes connect across different systems and contexts.
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Key Insight
Algorithmic bias in education is particularly dangerous because it can be invisible — a student routed to easier content, a teacher's assessment overridden, or a recommendation withheld — all without anyone realising the system is making different decisions for different groups. The first step to addressing bias is making it visible.