11–12 · Leadership, Ethics, Policy

AI Auditing

Unit 2 trains students to audit AI systems rigorously. Across five 45-minute lessons, they define an AI audit, set measurable criteria, expose bias and representation gaps, rank risks by severity, and write an audit report with recommendations.

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

The 5 lessons

  1. 1

    What Is an AI Audit?

    45 min

    Students define an AI audit and explain why structured reviews catch bias, errors, and risk.

    Vocabulary: audit · evaluate · risk · evidence

  2. 2

    Audit Criteria

    45 min

    Learners build clear, measurable criteria for judging an AI system fairly.

    Vocabulary: criteria · benchmark · measure · reliability

  3. 3

    Bias and Representation

    45 min

    Students analyze representation gaps in datasets and systems and why they cause unfair outcomes.

    Vocabulary: representation · bias · gap · inclusion

  4. 4

    Risk and Harm

    45 min

    Learners classify AI risks by severity — from inconvenient to genuinely harmful — and justify their ranking.

    Vocabulary: risk · harm · consequence · severity

  5. 5

    Audit Report

    45 min

    Students write a concise audit report that turns findings into clear recommendations and next steps.

    Vocabulary: report · recommendation · evidence · revise

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