error
What It Means
Error is the difference between what your data shows and what actually happened in reality. It comes in two forms: random fluctuations that happen by chance, and systematic biases that consistently push results in one direction.
Why Chief AI Officers Care
Errors directly impact business decisions by making AI systems unreliable, leading to poor strategic choices, failed product launches, regulatory compliance issues, and loss of customer trust when predictions don't match reality.
Real-World Example
A retail AI predicts 1000 jacket sales but actual sales are 800 - that's a 200-unit error that could be random market fluctuation or systematic bias like consistently overestimating demand in certain regions.
Common Confusion
People often think all errors are mistakes that can be eliminated, but some error is inevitable in any measurement or prediction system - the goal is to understand, minimize, and account for it in decision-making.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI systems, error directly impacts patient safety and diagnostic accuracy, making it a critical regulatory...
Finance: In finance, error manifests as model risk when predictive models (credit scoring, VaR calculations, algorithmic trading)...
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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 difference between the observed value of an index and its “true” value. Errors maybe random or systematic. Random errors are generally referred to as “errors”. Systematic errors are called “biases”."Source: OECD
"Difference between a computed, observed, or measured value or condition and the true, specified, or theoretically correct value or condition."Source: IEEE_Soft_Vocab
"measured quantity value minus a reference quantity value"Source: aime_measurement_2022, citing ISO/IEC Guide 99
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