ComputingArtificial IntelligenceAges 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

Lesson

Set out your lesson

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

Before teaching

Use this short check, then start teaching.

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.

Printer, credit, and teaching notes

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.

Next: Show

Adult extras

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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Teaching approaches

Gakuva’s recommended starting approach is selected. You can still compare every option.

1 approach

Selected Default Gakuva

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

Family records and proof options

Keep a record of your learning

Save photos, notes, and attendance. Export a proof packet for compliance or portfolio reviews.

View learner plans ($12/mo per learner) See sample family archive

Learning resources

Extra practice links — they do not replace Gakuva’s teaching approach.

Common Sense Education · Practice link — not the full lesson

Digital Citizenship Curriculum

Open practice(new tab)

Before you open · source notes

Digital Citizenship Curriculum supports Bias in AI Systems. An adult previews and guides; this is a learning page, not a scored quiz.

Free educational page, no sign-in required for the tested path.

Before you open

Ask the learner one fact they learned and one question they still have.

Opening this link does not save learning or assess mastery.

Source, access and review notes

Public Common Sense Education page. An anonymous GET on 2026-11-12 returned HTTP 200 with no login or payment prompt for this URL. Opening the link does not save practice or assess mastery. An adult should preview and guide the learner.

Common Sense Education. “Digital Citizenship Curriculum.” External page; Gakuva does not copy, host, embed, or redistribute materials.

Unknown — link only

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

Curriculum