Computing Artificial Intelligenceusually ages 9–11

Bias in AI Systems

If training data is biased, AI will be biased; examples: facial recognition working better for some skin tones, translation assuming gender; where bias comes from and whether we can fix it

How to tell they’ve got it

Tick these off as you see them — no test required.

Explain what bias in AI means using a real-world example
Describe how biased training data leads to biased AI results
Suggest one way to reduce bias in an AI system (use more diverse data, test with different groups)

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Try this together

Could your child explain why an AI trained mostly on photos of light-skinned faces might not work as well for people with darker skin?

Where this sits on the map

Stuck here? Check the skills it builds on first. Confident? Here’s what it unlocks.

Builds on

AI Mistakes and Limitationsages 7–9Must understand AI can be wrong before examining systematic bias

Machine Learning Basicsages 7–9Must understand training process before grasping how biased data creates biased AI

Bias in AI Systemsthis skill · ages 9–11

Unlocks

AI and Fairness in Decisionsages 9–11Must understand bias before examining fairness in high-stakes AI decisions

Designing Fair AI Rulesages 9–11Must understand bias before designing systems that avoid it

AI and the Future of Workages 9–11Bias awareness adds depth to understanding AI's impact on employment

solid = must come firstdashed = helps

Curriculum alignment

Candidate matches to official curriculum codes — machine-suggested, unreviewed (v0.1).

This skill sits outside the F–6 Australian Curriculum — no candidate code matched (v0.1). No NSW K–6 outcome code matched (v0.1). No Victorian Curriculum 2.0 code matched (v0.1).

Nearby on the map

AI and Fairness in Decisionsages 9–11 AI and the Environmentages 9–11 AI and the Future of Workages 9–11 AI Data Collection and Privacyages 9–11

All Artificial Intelligence skills →