false negative
This glossary entry explains false negative for AI governance and model risk programs. The sections below summarize what the term means in plain language, why chief AI officers and cross-functional committees track it, where teams often get confused, and—when you are signed in—how it shows up across major industries and in expectations tied to the EU AI Act and NIST AI RMF. Use related links at the end of the page to explore neighboring concepts without losing context.
What It Means
A false negative occurs when your AI system incorrectly says 'no' or misses something it should have caught. The system fails to identify or flag something that actually exists or should be detected. Think of it as your AI being overly cautious and missing real opportunities or threats.
Why Chief AI Officers Care
False negatives directly cost your business money through missed opportunities, undetected risks, or regulatory failures. In critical applications like fraud detection or medical diagnosis, missing real problems can lead to significant financial losses, legal liability, and damaged customer trust. Unlike false positives that create extra work, false negatives create invisible gaps in your operations.
Real-World Example
Your credit card fraud detection system fails to flag a $5,000 purchase made with a stolen card in another country, allowing the fraudulent transaction to go through. The system incorrectly classified this suspicious activity as legitimate, resulting in a direct financial loss and potential customer dispute.
Common Confusion
People often confuse false negatives with false positives - remember that false negatives are about missing the bad stuff, while false positives are about incorrectly flagging good stuff. False negatives are often harder to spot because you don't know what you're not seeing.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, a false negative occurs when diagnostic or screening systems fail to detect actual diseases, condition...
Finance: In finance, false negatives occur when AI systems fail to detect actual risks, fraudulent transactions, or regulatory vi...
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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
"An example in which the predictive model mistakenly classifies an item as in the negative class."Source: NSCAI
"an outcome where the model incorrectly predicts the negative class."Source: google_dev_classification-true-false-positive-negative
"A false negative is denying an applicant who should be approved"Source: Varshney,_Kush
"1. An instance in which a security tool intended to detect a particular threat fails to do so. 2. Incorrectly classifying malicious activity as benign."Source: CSRC_false_negative
Related Terms
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