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Computers & Digital Literacy · Grade 7

AI Models, Bias and Responsible Use

Learners look inside simple AI models. They train a toy message classifier by hand from labeled examples, watch it make confident mistakes that show it finds patterns rather than understanding, and use fictional test tables to see how unrepresentative training data produces biased results. They trace how a library recommender picks books from other borrowers' choices, then apply privacy, transparency, human review and disclosure to real-feeling decisions.

  • 5 lessons
  • About 115 minutes
  • AI models and responsible use check: 10 questions
  • Ways to answer: fill in the blanks, choose an answer, trace code, highlight a text, match pairs and type a number
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What learners will discover

  • Explain how a classifier learns patterns from labeled examples, and apply a simple learned rule to classify new cases.
  • Explain how training data shapes a model's outputs and why a model finds patterns without understanding like a person.
  • Use data tables to show how unrepresentative training data leads to worse results for some groups.
  • Explain how a recommendation system uses past behavior of similar users to suggest items, and name its benefits and risks.
  • Apply responsible-use principles (privacy, transparency, human review and disclosure) to AI situations.

Lessons

  1. Learning from labeled examples 6 steps4 practiceabout 24 min
  2. Patterns are not understanding 6 steps4 practiceabout 22 min
  3. Unrepresentative data and biased results 6 steps4 practiceabout 24 min
  4. How recommendation systems choose 6 steps3 practiceabout 22 min
  5. Using AI responsibly 5 steps4 practiceabout 23 min
  6. AI models and responsible use check 10 questions at the end

Keep exploring

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Guide for grown-ups