The AI PM interview question filtering candidates right now has nothing to do with AI.
It goes like this: "Your hiring tool recommends 15% fewer candidates from certain demographic backgrounds. Engineering says it's a data problem. Board presentation in two weeks. What's your plan?"
Most candidates walk straight into the trap. They start arguing root cause. Training data. Feature weights. Model architecture. The interview ends before they realize it.
The product answer starts somewhere else entirely. When the output is causing harm, root cause is secondary to stopping the harm. Pause auto-reject for the affected segment today. Human recruiters still see recommendations. No automated rejections until the audit closes.
Prasad Reddy learned this version of the lesson at Deaher, leading diagnostics products. When the system was returning incorrect diagnoses, the team didn't debug in production. They pulled the feature, routed everything to human review, and fixed the model offline. Twenty-six years as a CPO, same instinct every time: stop the harm, then find the source.
The math on why this matters: a bias audit adds 10 days. A class action costs years and hundreds of millions. EEOC has been filing AI discrimination cases since 2023 and the volume is rising. Enterprise customers now require bias audits before vendor selection.
For the board, you lead with it. "Found this. Here's our response. Here's the timeline." Boards punish surprises. They respect transparency.
The candidates passing this interview aren't the ones with the deepest AI knowledge. They're the ones who treat a 15% recommendation gap as a liability decision before it becomes a technical debate.
Pause first. Investigate second.
That sequencing is the whole interview.
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