domain shift
What It Means
Domain shift occurs when an AI model trained on one type of data is applied to a different but related context where the data characteristics have changed. It's like training a sales forecasting model on retail data from 2019, then trying to use it to predict sales during the pandemic - the underlying patterns and relationships in the data have shifted significantly.
Why Chief AI Officers Care
Domain shift can cause AI models to fail silently in production, leading to poor business decisions based on inaccurate predictions. It creates ongoing maintenance costs as models need retraining when business conditions change, and can expose the organization to compliance risks if model performance degrades without proper monitoring.
Real-World Example
A credit scoring model trained on loan applications from urban areas performs poorly when deployed in rural markets because income patterns, employment types, and spending behaviors are fundamentally different between these populations, even though both involve credit decisions.
Common Confusion
People often confuse domain shift with simple data drift or model decay over time, but domain shift specifically involves deploying models across different contexts or environments rather than just temporal changes in the same environment.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, domain shift occurs when AI models trained on one patient population, hospital system, or data collection...
Finance: In finance, domain shift frequently occurs when credit risk models trained on pre-recession data fail during economic do...
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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
"Differences between the source and target domain data"Source: Stacke,_Karin
Related Terms
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