type II error
What It Means
A type II error occurs when your AI system fails to detect something important that is actually there. It's like a security system that doesn't alert you when there's actually a break-in, or a fraud detection system that lets fraudulent transactions slip through unnoticed.
Why Chief AI Officers Care
Type II errors create direct business losses through missed opportunities and undetected problems. In critical applications like medical diagnosis, financial fraud, or safety monitoring, failing to catch real issues can result in regulatory violations, liability exposure, and significant financial damage. The cost of missing true positives often far exceeds the cost of false alarms.
Real-World Example
A credit card company's fraud detection AI has a type II error rate of 15%, meaning it fails to flag 15% of actual fraudulent transactions. While customers appreciate fewer false alerts blocking legitimate purchases, the company loses millions annually to undetected fraud that the system should have caught but didn't.
Common Confusion
People often confuse type II errors with type I errors - type II is missing real problems (false negatives), while type I is raising false alarms (false positives). Many assume reducing false alarms automatically improves the system, but this often increases the more costly problem of missing real threats.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, a type II error occurs when diagnostic or screening systems fail to detect actual diseases or conditio...
Finance: In finance, a type II error occurs when AI systems fail to flag actual instances of fraud, money laundering, suspicious ...
Premium content locked
Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"The null hypothesis H0 is accepted, even though it is [false]"Source: berthold_guide_2020
"true positive rate"Source: james_statistical_2014
Discuss This Term with Your AI Assistant
Ask how "type II error" applies to your specific use case and regulatory context.
Start Free Trial