Research Note

False Positives vs False Negatives

AI systems do not fail in identical ways. Different deployment contexts place different importance on false positives and false negatives.

As a result, model evaluation cannot rely on accuracy alone. Understanding which errors matter most is essential for deployment assurance.

Error Trade-offs

In many systems, reducing one type of error increases another. Lowering false positives may increase false negatives, while aggressively reducing false negatives may increase false alarms.

These trade-offs become particularly important in high-stakes operational environments.

Context Changes Risk

The same model behaviour may be acceptable in one domain but unacceptable in another.

For example, a false positive in a recommendation system may have minimal impact, while a false positive in facial recognition, healthcare, or security environments may carry substantial consequences.

Deployment Assurance Requires Context

Effective AI assurance increasingly depends on:

• Error trade-off analysis
• Threshold sensitivity evaluation
• Operational risk assessment
• Subgroup-level error analysis
• Context-aware deployment review

Understanding which errors matter and under which conditions is central to evaluating real-world AI deployment suitability.