Your model passes bias checks for gender and for race separately, but fails for Black women. How do you handle intersectional fairness?
Single-axis fairness audits are just another form of averaging, and they average away the exact group that is being harmed. The hard part is not noticing that: it is handling the combinatorial blowup, the tiny cells, and the multiple-testing problem without chasing ghosts.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Single-axis fairness audits are just another form of averaging, and they average away the exact group that is being harmed. The hard part is not noticing that: it is handling the combinatorial blowup, the tiny cells, and the multiple-testing problem without chasing ghosts.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
