Artificial intelligence · Grades 9–12
Responsible AI, fairness and verification · revision notes
Describe who uses the system, who may be affected, and who can correct mistakes. Automation does not remove human responsibility.
Key ideas and conditions
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.
Explain without looking
- Explain why a generated rationale does not necessarily reveal the actual internal cause of a model's output.
- Which claim should be avoided without evidence?
Further reading and review references
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