criterion validity
What It Means
Criterion validity measures how well your AI system's predictions or assessments match up with real-world outcomes that actually matter to your business. It's about proving your AI tool can accurately predict future performance, correctly identify current situations, or align with established gold-standard measures that you already trust.
Why Chief AI Officers Care
Without criterion validity, your AI investments become expensive guesswork that could lead to poor hiring decisions, failed product launches, or regulatory violations. It's essential for demonstrating ROI to executives and ensuring your AI systems actually improve business outcomes rather than just producing impressive-looking but meaningless scores.
Real-World Example
A company builds an AI system to screen job candidates and claims it predicts job performance with 95% accuracy. To establish criterion validity, they need to track whether candidates the AI rated highly actually perform better in their roles six months later (predictive validity) and whether the AI's scores align with current performance reviews from managers (concurrent validity).
Common Confusion
People often confuse criterion validity with accuracy metrics like precision or recall, but criterion validity specifically requires validation against meaningful real-world outcomes, not just technical performance on test datasets that may not reflect actual business value.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, criterion validity is essential for demonstrating that your predictive models accurately correlate wit...
Finance: In finance, criterion validity is essential for demonstrating that AI models can accurately predict actual financial out...
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- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"compares responses to future performance or to those obtained from other, more well-established surveys. Criterion validity is made up two subcategories: predictive and concurrent. Predictive validity refers to the extent to which a survey measure forecasts future performance. A graduate school entry examination that predicts who will do well in graduate school has predictive validity. Concurrent validity is demonstrated when two assessments agree or a new measure is compared favorably with one that is already considered valid."Source: fink_survey_2010
"an index of how well a test correlates with an established standard of comparison (i.e., a criterion). Criterion validity is divided into three types: predictive validity, concurrent validity, and retrospective validity. For example, if a measure of criminal behavior is valid, then it should be possible to use it to predict whether an individual (a) will be arrested in the future for a criminal violation, (b) is currently breaking the law, and (c) has a previous criminal record."Source: APA_criterion_validity
Related Terms
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