true negative
What It Means
A true negative occurs when an AI model correctly identifies something as NOT belonging to a category you're looking for. It's like a security system that correctly determines a person is NOT a threat, or a fraud detection system that correctly identifies a transaction as NOT fraudulent.
Why Chief AI Officers Care
True negatives represent efficient operations - they show your AI isn't creating unnecessary alerts, investigations, or interventions that waste resources. High true negative rates mean your systems are accurately filtering out normal cases, allowing teams to focus on actual issues rather than false alarms.
Real-World Example
A loan approval AI reviews 1,000 applications and correctly identifies 800 applicants as NOT high-risk (they won't default). These 800 true negatives allow the bank to process approvals quickly without manual review, reducing processing time and costs while maintaining lending standards.
Common Confusion
People often overlook true negatives because they represent 'nothing happening' - but they're actually crucial for measuring system efficiency. They're frequently confused with true positives, but true negatives are about correctly identifying what you DON'T want, not what you do want.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, a true negative occurs when a diagnostic model correctly identifies a patient as NOT having a specific...
Finance: In finance, a true negative occurs when an AI model correctly identifies a transaction, customer, or entity as NOT requi...
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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
"outcome where the model correctly predicts the negative class."Source: google_dev_classification-true-false-positive-negative
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