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
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1858 concepts · 9 subjects
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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.
Materials: Two short recommendation cards or screenshot printouts; Pencil for compare notes. 1. Say the goal of AI and Fairness in Decisions in plain words once. 2. Adult shows one AI suggestion and asks what data might have caused it. 3. Child reviews three AI examples and names one input clue and one possible bias or limit each. 4. Fresh check: one fresh AI example with a limit. Record and stop. Done: three AI examples with an input clue and a limit note. Proof: parent note of examples and results. Stop: End after the fresh check; keep optional challenge for another day.
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Start practiceAI and Fairness in Decisions practice
Practice key ideas from AI and Fairness in Decisions.
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Learning resources
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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