false positive
This glossary entry explains false positive 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 positive occurs when an AI system incorrectly identifies something as a problem or positive result when it actually isn't. It's like a smoke detector going off when you're just cooking dinner - the system thinks it found something important, but it's wrong.
Why Chief AI Officers Care
False positives waste resources, frustrate customers, and can damage trust in AI systems. They lead to unnecessary investigations, blocked legitimate transactions, flagged content that shouldn't be, and employee time spent on non-issues.
Real-World Example
A fraud detection system flags a legitimate customer purchase as suspicious and blocks their credit card, forcing them to call customer service and potentially shop elsewhere due to the inconvenience.
Common Confusion
People often think more sensitive AI detection is always better, but increasing sensitivity typically increases false positives - there's usually a trade-off between catching real problems and avoiding false alarms.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, false positives occur when diagnostic algorithms incorrectly flag healthy patients as having a disease...
Finance: In finance, false positives occur when AI systems incorrectly flag legitimate transactions as fraudulent, compliant cust...
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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 model mistakenly classifies an item as in the positive class"Source: NSCAI
"an outcome where the model incorrectly predicts the positive class."Source: google_dev_classification-true-false-positive-negative
" A false positive is approving an applicant who should be denied"Source: Varshney,_Kush
"1. An alert that incorrectly indicates that a vulnerability is present. 2. An alert that incorrectly indicates that malicious activity is occurring. 3. An instance in which a security tool incorrectly classifies benign content as malicious. 4. Incorrectly classifying benign activity as malicious. 5. An erroneous acceptance of the hypothesis that a statistically significant event has been observed. This is also referred to as a type 1 error. This is also referred to as a type 1 error. When “health-testing” the components of a device, it often refers to a declaration that a component has malfunctioned – based on some statistical test(s) – despite the fact that the component was actually working correctly."Source: CSRC_false_positive
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