Bias in AI Systems

ComputingArtificial IntelligenceAges 9–11

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

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Teaching Table

Set out your lesson

Practise the bounded task in Bias in AI Systems; see exact limits before inferring broader coverage.

Activity

Can compare error counts with equal test-group sizes; use fictional shapes, never real faces or demographic data.

Policy

Exact directed source edges retained; earlier authorship is not learner attainment.

Materials

These supplied stage cards
Scrap paper and pencil; oral/pointing response where appropriate

Sequence

Read readiness and decide on support without treating prerequisite authoring as learner readiness.
Open Show first and explain its model.
Use Guided Try with support and its separate adult key.
Close Show and guided keys before Try, unless this Try is explicitly supported.
Reflect on an actual attempt, including help used.

No Printer

Use the supplied screen cards; copying the exact task to paper is optional, not required.

Privacy

Use only invented scenarios and optional anonymous local notes. Do not request names, photos, family finances, medical data, accounts or uploads. Stop uncomfortable physical activity.

Use the complete materials on screen or print only the current sheet. Keep adult guidance out of learner view. Retain an earlier model only when the current step supplies it as context.

Teaching limits and safe use

These are finite task opportunities, not proof of mastery. Record what was attempted and what support was used.

Toy data-bias analogy only. Primary-source real-world face-recognition search failed, so the exact real-example evidence clause is blocked rather than fabricated. No product-specific or demographic performance claim verified.

Use only invented scenarios and optional anonymous local notes. Do not request names, photos, family finances, medical data, accounts or uploads. Stop uncomfortable physical activity.

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Entire curriculum

1590 concepts · 8 subjects

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Practise the bounded task in Bias in AI Systems; see exact limits before inferring broader coverage.

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Learning resources

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Check understanding

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)

“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?”

Curriculum record

Type: Conceptual
Subject: Computing
Domain: Artificial Intelligence
Age range: Ages 9–11