9–10 · Audit, Policy, Accountability

Bias in Data and Models

Unit 2 trains students to audit AI for fairness. Across five 45-minute lessons, they identify bias in data and models, run a dataset audit, propose fairness improvements, and defend their fixes with evidence.

📘 5 lessons · 45 min lessons📄 5 printable activities + certificate

The 5 lessons

  1. 1

    What Is Bias in Data?

    45 min

    Students identify bias in a dataset and explain how incomplete or unbalanced data skews AI decisions.

    Vocabulary: bias · representation · unbalanced · fairness

  2. 2

    Bias in Models

    45 min

    Learners explain how a model's outputs can reflect hidden bias inherited from its training data.

    Vocabulary: model · pattern · impact · unfair

  3. 3

    Audit the Dataset

    45 min

    Students run a simple dataset audit, highlighting gaps and imbalance with evidence.

    Vocabulary: audit · gap · representation · evidence

  4. 4

    Improve Fairness

    45 min

    Learners propose dataset changes — more representative examples, better labels — to improve fairness.

    Vocabulary: fairness · revise · balance · improve

  5. 5

    Defend the Fix

    45 min

    Students defend their fairness improvements with evidence and weigh the tradeoffs.

    Vocabulary: justify · evidence · defend · tradeoff

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