Precision vs Recall in Practice: Lowering False Alarms and Misses in an AI Feature
Company: Palo Alto Networks
Role: Product Manager
Category: Machine Learning
Difficulty: medium
Interview Round: Other
You are the product manager for an AI product that gets things wrong in both directions: it flags or acts on items it should leave alone, and it lets through items it should catch. How would you reduce both its false positives and its false negatives?
```hint Price each mistake
Before touching the model, work out what one false positive and one false negative each cost the user and the business in this product. That tells you which error to push down first and how far.
```
### Clarifying Questions
- Which product and decision are we discussing: a classifier that blocks suspicious emails or files, a content filter, or a generative assistant whose answers can be wrong?
- What does each error cost here: does a false negative let real harm through, while a false positive only annoys or blocks a legitimate user?
- Do we have labeled outcomes, such as analyst verdicts, user reports or appeals, for both error types, or only signals for one of them?
- Is a human reviewing some of the decisions, and is there capacity to route uncertain cases to that reviewer?
### What a Strong Answer Covers
- Precise definitions of a false positive and a false negative for the chosen product, with a cost attached to each.
- How both error types are measured, including misses nobody reports, and why the metric choice matters on imbalanced data.
- Diagnosing where the errors come from before choosing fixes.
- The levers available, from threshold and decision design to data, labels, features and model changes, and what each does to the two error types.
- Shipping changes safely and keeping error rates from creeping back after launch.
### Follow-up Questions
- Leadership asks for zero false negatives. How do you respond, and what would that target cost users?
- After a model update, offline precision improves, yet customers report more legitimate items being blocked. How do you investigate?
- How do you estimate the false negative rate when, by definition, nobody flagged those cases?
Overview: A product manager interview question about lowering both false positives and false negatives in an AI product. It tests understanding of precision, recall and the cost of each error type, and the ability to measure hidden misses, diagnose their causes, and ship improvements safely.