AI and Fairness in Decisions
Whether AI should make important decisions about people: jobs, loans, justice; who is responsible when AI makes unfair decisions; introduction to algorithmic fairness
Lesson
Set out your lesson
“AI and Fairness in Decisions means practising the idea at the table with a simple screen-free or sandbox check. We will demo once, then your child tries.”
Bring to the table
- Two short recommendation cards or screenshot printouts
- Pencil for compare notes
Extra for the adult
Worked example. A feed keeps showing football because past clicks were football — that can hide other topics (filter bubble).
Optional challenge. For AI and Fairness in Decisions, add one harder example after success.
What to notice. Keep one parent note: examples tried; results; support used. This session samples AI and Fairness in Decisions, not mastery.
Share and support
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.
Practice this concept
Start practiceAI and Fairness in Decisions practice
Practice key ideas from AI and Fairness in Decisions.
Free · no login · no ads · Practice answers and completion are not saved. Parent observation remains the lesson record.
Learning resources
Extra practice links — they do not replace Gakuva’s teaching approach.
Code.org · Practice link — not the full lesson
AI for Oceans
Before you open · source notes
AI for Oceans supports AI and Fairness in Decisions. 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 openAsk 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 Code.org 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.
Code.org. “AI for Oceans.” External page; Gakuva does not copy, host, embed, or redistribute materials.
Unknown — link only
Check understanding
- Give an example of an important decision that AI might help make (hiring, medical diagnosis, loan approval)
- Explain why letting AI make decisions about people can be risky if the system is biased
- Suggest who should be responsible if an AI system makes an unfair decision
“If your child learned that a computer program was deciding who gets a job interview, could they explain why that might be unfair and who should be responsible?”
Curriculum record
- Type
- Conceptual
- Subject
- Computing
- Domain
- Artificial Intelligence
- Age range
- Ages 9–11