Many teams now use AI tools to source and screen candidates. How would you decide where to use them and how to keep them fair?

What the interviewer is looking for

• This question tests recruiting data & tools. Uses an applicant tracking system well, tracks metrics like time to fill and pass-through rates, and uses AI tools thoughtfully with fairness checks.
• Strong answers: Uses AI for efficiency tasks with human decisions, audits outcomes for adverse impact, checks legal requirements such as local bias-audit and AI rules, is transparent with candidates and monitors over time.
• Likely follow-up: What would you do if an audit showed one group was being screened out more often?

Another way it may be asked: Your company wants to use AI to help pick applicants. Where would you use it, and how would you check it treats people fairly?

A structure to follow: Frame, structure, analyse, recommend

1. Step 1

   Frame

   Restate the goal and ask what success is and what the constraints are.

2. Step 2

   Structure

   Split the problem into a few separate parts you can check one by one, and say what you will look at first and why.

3. Step 3

   Analyse

   Reason with the information you have, state your assumptions out loud, and say what data would change your mind.

4. Step 4

   Recommend

   Give a clear recommendation, the main risk, and the first step you would take.

This is guidance on shape, not a script. Use your own real experience and facts; never invent details to fill a gap. See the STAR guide and answer frameworks.

Common slips

• Listing ideas without choosing between them.
• Hiding assumptions instead of stating them.
• Finishing without a recommendation.

How to practise it

Answer in four spoken parts, one per step, then check you ended on a recommendation.

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