feature shift
What It Means
Feature shift occurs when individual data inputs to an AI system start behaving differently than expected, even if the overall data patterns look normal. It's like detecting that one ingredient in a recipe has changed quality, even when the final dish still looks the same.
Why Chief AI Officers Care
Feature shifts can signal cyberattacks, equipment malfunctions, or data manipulation attempts that could cause AI systems to make costly wrong decisions before the problems show up in business metrics.
Real-World Example
A fraud detection system might notice that transaction location data has subtly changed patterns (like GPS coordinates being slightly off) even though transaction amounts and merchant types look normal - indicating potential GPS spoofing attacks.
Common Confusion
People often think monitoring overall system performance is enough, but feature shift detection catches problems in individual data sources that attackers might manipulate while keeping the big picture looking normal.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI systems, feature shift occurs when clinical data inputs like lab values, vital signs, or imaging charac...
Finance: In finance, feature shift occurs when key model inputs like credit scores, interest rates, or market volatility indicato...
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
"Unlike joint distribution shift detection, which cannot localize which features caused the shift, we define a new hypothesis test for each feature individually. Naïvely, the simplest test would be to check if the marginal distributions have changed for each feature (as explored by [25]); however, the marginal distribution would be easy for an adversary to simulate (e.g., by looping the sensor values from a previous day). Thus, marginal tests are not sufficient for our purpose. Therefore, we propose to use conditional distribution tests. More formally, our null and alternative hypothesis for the j-th feature is that its full conditional distribution (i.e., its distribution given all other features) has not shifted for all values of the other features."Source: kulinski_feature_2020
Discuss This Term with Your AI Assistant
Ask how "feature shift" applies to your specific use case and regulatory context.
Start Free Trial