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Artificial intelligence · Grades 9–12

Responsible AI, fairness and verification

Describe who uses the system, who may be affected, and who can correct mistakes. Automation does not remove human responsibility.

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Prerequisites

Basic AI concepts and critical reading.

Study notes

Accountability

Describe who uses the system, who may be affected, and who can correct mistakes. Automation does not remove human responsibility.

Bias

Data coverage and measurement choices can cause uneven performance. Examine relevant groups and error types without assuming one aggregate metric settles fairness.

Verification

Check important factual outputs against reliable sources. Do not invent citations or treat a generated explanation as proof of the model’s internal process.

Boundaries

Protect private data, respect rights and require appropriate human review for consequential uses. These are design principles, not a guarantee of legal compliance.

Worked examples

Does equal overall accuracy guarantee fair outcomes for every relevant group?

Aggregate metrics can hide different error patterns and contextual harms.

No

What should happen when an AI output cites a source?

Citations can be incorrect or fail to support a claim.

Verify that the source exists and supports the claim

Check your understanding

  1. In a toy audit, group A has 8 errors in 40 cases. Find error rate.

  2. Group B has 3 errors in 30 cases. Find error rate.

  3. Why should a system offer a route for reviewing or correcting mistakes?

  4. Which is an appropriate privacy practice for a classroom exercise?

    1. Assume anonymization is always perfect
    2. Use authorized or synthetic data without unnecessary personal details
    3. Publish all raw identifiers
    4. Upload private records without permission

Answers and marking guidance

Answers and marking notes
  1. 0.2

    8/40=0.2.

  2. 0.1

    3/30=0.1; comparison still needs context and uncertainty.

  3. Affected people and operators need a way to challenge errors, fix data and prevent repeated harm. Responsibility must be operational, not merely a slogan.

    Credit a concrete correction and accountability pathway.

  4. Use authorized or synthetic data without unnecessary personal details

    Minimize exposure and use data appropriately.

Further reading and review references

These are reference links, not copied exercises, institutional endorsement or proof of independent review.

AI-assisted preview; independent educational review required. Level labels describe prerequisites, not accreditation or official curriculum alignment.