Making precision and recall tradeoffs

Precision and recall represent two different ways of measuring AI accuracy. Precision asks "Of all the things the AI flagged, how many were correct?" Recall asks "Of all the things that should have been flagged, how many did the AI catch?" You can't maximize both simultaneously.

Picture a spam filter. High precision means almost every email it marks as spam truly is spam. Users trust the filter and rarely check their spam folder. But it might let some spam through to the inbox. High recall means catching nearly all spam, but some legitimate emails get caught too. Users must regularly check their spam folder for false positives.

The right balance depends on user needs:

• Email systems often favor recall to protect users from scams
• Medical screening tests favor recall to avoid missing diseases
• Legal document search favors precision to reduce review time
• Product recommendations balance both for relevance and discovery

Understanding your users helps make this choice. Busy professionals might prefer high precision to avoid sorting through irrelevant results. Researchers might want high recall to ensure they don't miss important findings.