variance
What It Means
Variance measures how spread out your data points are from the average - basically how consistent or inconsistent your results are. If your AI model predictions have low variance, they cluster tightly around the expected outcome; high variance means predictions are scattered widely, indicating unpredictable performance.
Why Chief AI Officers Care
High variance in AI systems creates business risk through unpredictable outcomes that can damage customer trust and regulatory compliance. It makes it difficult to set reliable performance expectations, budget for outcomes, or explain AI decisions to stakeholders and auditors.
Real-World Example
A credit scoring AI model shows low variance when it consistently rates similar loan applicants within a narrow range (say 720-740), but high variance when identical profiles get wildly different scores (anywhere from 650-800), making it unreliable for business decisions.
Common Confusion
People often confuse variance with bias - variance is about consistency of results while bias is about systematic errors in one direction. You can have a model that's consistently wrong (high bias, low variance) or inconsistently right (low bias, high variance).
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, variance directly impacts patient safety and regulatory compliance - models with high variance may per...
Finance: In finance, variance measures the dispersion of returns around their mean, serving as a key risk metric where higher var...
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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
"The variance is the mean square deviation of the variable around the average value. It reflects the dispersion of the empirical values around its mean."Source: OECD
"A quantifiable deviation, departure, or divergence away from a known baseline or expected value"Source: IEEE_Soft_Vocab
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