recall
What It Means
Recall measures how good your AI model is at finding all the things you're actually looking for. It answers the question: 'Out of all the real cases that exist, what percentage did my model successfully catch?' A model with high recall rarely misses true positives, but might flag too many false alarms.
Why Chief AI Officers Care
Recall is critical for high-stakes applications where missing something important could be catastrophic - like failing to detect fraud, missing medical diagnoses, or overlooking security threats. Poor recall means your AI is letting real problems slip through the cracks, which can lead to financial losses, regulatory violations, or safety incidents. CAIOs must balance recall against precision to avoid both missed opportunities and alert fatigue.
Real-World Example
A bank's fraud detection system has 95% recall, meaning it catches 95 out of every 100 actual fraudulent transactions. However, the 5% it misses could represent millions in losses. The CAIO must decide whether to tune the model for higher recall (catching more fraud but generating more false alarms that annoy customers) or accept the current miss rate to maintain customer experience.
Common Confusion
People often confuse recall with accuracy or precision. High recall doesn't mean your model is accurate overall - it just means it's good at not missing positive cases, even if it also flags many false positives along the way.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, recall is critical for diagnostic and screening applications where missing a positive case (like cance...
Finance: In finance, recall is critical for regulatory compliance models like anti-money laundering (AML) and fraud detection, wh...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"A metric for classification models; identifies the frequency with which a model correctly classifies the true positive items."Source: NSCAI
"A metric for classification models that answers the following question: Out of all the possible positive labels, how many did the model correctly identify? That is: Recall = True Positive/( True Positive + false Negative)"Source: aime_measurement_2022, citing Machine Learning Glossary by Google
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