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Interview question · AI Governance, Risk & Compliance

How do you audit fairness and bias in a high-risk model, such as one used in hiring or credit?

Fairness has several incompatible definitions, so the audit starts with a decision, not a metric. The scored answer picks measures for the context, gets the data to compute them, and audits the whole decision process.

hardNewPreparation tags: JPMorgan Chase, Microsoft, GoogleUpdated Nov 2026

Fairness has several incompatible definitions, so the audit starts with a decision, not a metric. The scored answer picks measures for the context, gets the data to compute them, and audits the whole decision process.

Make your first attempt

What would you say before reading the answer?

  1. 01 State the assumptions your answer depends on.
  2. 02 Explain the mechanism and choose a control.
  3. 03 Name a condition that would change your decision.

The worked answer includes an approach, scoring criteria and follow-up questions. Compare the reasoning with your own.

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What if · the follow-ups in this answer

The answer works through 6 follow-ups like an interviewer would

A credit scoring model is classed as high-risk under the EU AI Act and needs a fairness audit before an update ships.

  1. Where does the audit start?
  2. How do you get the data to measure group outcomes?
  3. What would you measure?
  4. What if the model is fair but the process is not?
  5. What if the update changes disparities?
  6. What would the audit report contain?

Each rung has a nudge, a model answer, the principle that carries over and the weak answer to avoid. Premium opens every answer and ladder.

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