precision
What It Means
Precision measures how often your AI model is right when it makes a positive prediction. If your fraud detection system flags 100 transactions as fraudulent, precision tells you what percentage of those 100 were actually fraud. It's about the accuracy of your positive predictions, not how many actual fraud cases you missed.
Why Chief AI Officers Care
Low precision means your AI system creates too many false alarms, which wastes resources and frustrates users. In fraud detection, low precision means investigating hundreds of legitimate transactions. In hiring AI, it means rejecting qualified candidates. High precision reduces operational costs and maintains user trust in your AI systems.
Real-World Example
A loan approval AI has 80% precision, meaning that out of every 100 loan applications it approves, 80 will actually be good borrowers who repay on time, while 20 will default. The bank knows that 1 in 5 AI-approved loans will likely fail, helping them budget for expected losses and adjust lending criteria.
Common Confusion
People often confuse precision with recall - precision is about being right when you say 'yes,' while recall is about catching all the actual 'yes' cases. You can have high precision but still miss most of the problems you're trying to detect.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, precision is critical for diagnostic and screening systems where false positives can lead to unnecessa...
Finance: In finance, precision is critical for regulatory compliance and operational efficiency, as false positives in fraud dete...
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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. Precision identifies the frequency with which a model was correct when classifying the positive class."Source: NSCAI
"closeness of agreement between indications or measured quantity values obtained by replicate measurements on the same or similar objects under specified conditions"Source: aime_measurement_2022, citing ISO/IEC Guide 99
"A metric for classification models. Precision identifies the frequency with which a model was correct when predicting the positive class. That is: Precision = True Positive /(True Positive + False Positive)"Source: aime_measurement_2022, citing Machine Learning Glossary by Google
"Closeness of agreement between independent test results obtained under prescribed conditions. It is generally dependent on analyte concentration, and this dependence should be determined and documented. The measure of precision is usually expressed in terms of imprecision and computed as a standard deviation of the test results. Higher imprecision is reflected by a larger standard deviation. Independent test results means results obtained in a manner not influenced by any previous results on the same or similar material. Precision covers repeatability and reproducibility [19]. Alternatively, precision is a measure for the reproducibility of measurements within a set, that is, of the scatter or dispersion of a set about its central value. Precision depends only on the distribution of random errors and does not relate to the true value or specified value. "Source: UNODC_Glossary_QA_GLP
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