label
What It Means
A label is the correct answer or outcome that you're trying to teach an AI system to predict. It's the ground truth data that tells the AI what the right result should be for each example during training.
Why Chief AI Officers Care
Label quality directly determines AI system accuracy and business outcomes - poor labels lead to unreliable predictions that can damage customer trust and regulatory compliance. The cost and complexity of obtaining high-quality labels often represents the largest bottleneck in AI project timelines and budgets.
Real-World Example
In a fraud detection system, the label would be 'fraudulent' or 'legitimate' for each transaction. If your team incorrectly labels legitimate transactions as fraud during training, the AI will block valid customer purchases in production, directly impacting revenue.
Common Confusion
People often confuse labels with features - labels are what you're predicting (the answer), while features are the input data used to make the prediction. Labels are only needed during training, not when the model makes real predictions.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, labels represent clinically validated outcomes such as diagnoses, treatment responses, or risk classif...
Finance: In finance, labels represent the target outcomes for predictive models, such as "default" vs "no default" for credit ris...
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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
"A value corresponding to an outcome."Source: AI_Fairness_360
"target variable assigned to a sample"Source: aime_measurement_2022, citing ISO/IEC 22989
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