Lesson 10 of 12
Structured learning draftRisk review with Disparate Impact
In Algorithmic Bias & Fairness, the way a learner handles risk review shapes how Disparate Impact is used and evaluated. Risk review prioritises harms and tests controls. This intermediate lesson focuses on a decision or output that another person can inspect.
Learning objectives
- Explain risk review in the context of Algorithmic Bias & Fairness.
- Apply Disparate Impact to a bounded practical task.
- Evaluate the result using explicit quality criteria.
Risk Review: from context to evidence
Risk Review connects affected people and context to a documented mitigation in Algorithmic Bias & Fairness.
Define the purpose, intended user and Disparate Impact constraints.
Use affected-stakeholder and technical evidence.
Compare the observed result with a normal case, boundary case and stated limitation.
Review the evidence, adjust the method, and repeat.
Risk review prioritises harms and tests controls. For Disparate Impact, distinguish performing an operation from demonstrating that it suits the stated purpose. Use affected-stakeholder and technical evidence. Record assumptions that could change the conclusion.
Apply risk review deliberately
- State the Algorithmic Bias & Fairness task and the decision it supports.
- Prepare a small Disparate Impact case with a known input and difficult boundary.
- Use affected-stakeholder and technical evidence.
- Compare the observed result with the expected behaviour and explain differences.
- Save the evidence, limitation and next action in a review record.
A worked Disparate Impact evidence path
A four-step worked example for applying risk review to Disparate Impact, including a boundary test and revision.
Preserve the original Disparate Impact case and expected result.
Confirm the basic path behaves as expected.
Expose an assumption in the risk review method.
Change the method, rerun both cases and record the limitation.
| Review point | Evidence |
|---|---|
| Purpose | The specific Disparate Impact outcome and intended user |
| Method | The risk review decision, input and version or context |
| Result | Observed output plus a checked boundary case |
| Limitation | What the result does not establish and the next safe action |
Common mistakes
- Using Disparate Impact before defining what risk review must achieve.
- Checking only the easiest Algorithmic Bias & Fairness example.
- Reporting a result without its input, assumptions or limitation.
Practice activity
Apply the lesson
For Algorithmic Bias & Fairness, complete a bounded Disparate Impact task demonstrating risk review. Keep the original input, numbered method, normal test, boundary test, observed results and a 100-word self-review naming one limitation and next improvement.
Check your understanding
In Algorithmic Bias & Fairness, which evidence best supports a risk review result produced with Disparate Impact?
Lesson summary
- For Algorithmic Bias & Fairness, risk review means: Risk review prioritises harms and tests controls.
- A credible Disparate Impact result includes a checked boundary, not only a successful example.
- The next lesson builds on this risk review evidence record.
Sources and further reading
- Recommendation on the Ethics of AIUNESCO - accessed 2026-08-21
- AI PrinciplesOECD - accessed 2026-08-21
Personal study note