Computing // Artificial Intelligence

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

Ages
9 to 11
Type
Concept
Builds on
2 topics
Leads to
3 topics

Quick check

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

Ask this to see whether the topic is secure.

Topic // Mastery

What mastery looks like

What a learner can do once this topic is secure.
  • 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)

Topic // Map

Where this fits

What to learn first and what this topic opens up, with the reason for each link. Required links come first.
  • RequiredMust come first
  • HelpfulHelps, but not essential

Before this

2 topics
  • Machine Learning BasicsRequired

    Artificial Intelligence // Ages 7 to 9

    Must understand training process before grasping how biased data creates biased AI

  • AI Mistakes and LimitationsRequired

    Artificial Intelligence // Ages 7 to 9

    Must understand AI can be wrong before examining systematic bias

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Bias in AI Systems

Ages 9 to 11 // Concept

Leads to

3 topics
  • AI and Fairness in DecisionsRequired

    Artificial Intelligence // Ages 9 to 11

    Must understand bias before examining fairness in high-stakes AI decisions

  • Designing Fair AI RulesRequired

    Artificial Intelligence // Ages 9 to 11

    Must understand bias before designing systems that avoid it

  • AI and the Future of WorkHelpful

    Artificial Intelligence // Ages 9 to 11

    Bias awareness adds depth to understanding AI's impact on employment

Computing // Artificial Intelligence

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