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Artificial intelligence · lesson 4 of 4

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.

Grades 9–128 practice questionsNotes + worked examples
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Learn the ideas

Prerequisites: Basic AI concepts and critical reading.

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

Example 1

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

Aggregate metrics can hide different error patterns and contextual harms.

No

Example 2

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

8 questions

Decimals or fractions are accepted for numeric answers. Proofs and written responses use sample answers and require human marking. This is practice, not a protected examination.

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

Show answer and explanation

No

Aggregate metrics can hide different error patterns and contextual harms.

2 What should happen when an AI output cites a source?

Show answer and explanation

Verify that the source exists and supports the claim

Citations can be incorrect or fail to support a claim.

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

Show answer and explanation

0.2

8/40=0.2.

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

Show answer and explanation

0.1

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

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

Sample answer and marking guidance

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.

6 Which is an appropriate privacy practice for a classroom exercise?

Show answer and explanation

Use authorized or synthetic data without unnecessary personal details

Minimize exposure and use data appropriately.

7 Explain why a generated rationale does not necessarily reveal the actual internal cause of a model's output.

Sample answer and marking guidance

It is itself generated text and may be a plausible explanation rather than a faithful trace of computation. Independent analysis is needed for causal claims.

Credit explanation versus verified mechanism.

8 Which claim should be avoided without evidence?

Show answer and explanation

This AI system is universally unbiased

Universal fairness claims exceed what finite evaluations establish.

Further reading and review references

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