Shadow Evaluation
Definition
Shadow evaluation measures a new variant in parallel to the effective existing system.
An evaluation design that measures outputs of a system running in shadow deployment based on real production requests, while the existing system makes the effective decision.
Simply explained
Both solutions handle the same task, but only the old one decides; the new one is observed for comparison.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A new ranking is calculated in parallel and compared against click and quality metrics.
Limits and pitfalls
- Equating shadow evaluation with online A/B testing, using feedback from the existing policy as unbiased ground truth, ignoring counterfactual selection bias, or hiding latency and costs.
Origin & history
The approach combines shadow deployment with model and decision evaluation.
Use in mid-sized companies
- Possible application: A new ranking is calculated in parallel and compared against click and quality metrics.
Frequently asked questions
What does shadow evaluation mean?
An evaluation design that measures outputs of a system running in shadow deployment based on real production requests, while the existing system makes the effective decision. What mistakes should be avoided? Equating shadow evaluation with online A/B testing, using feedback from the existing policy as unbiased ground truth, ignoring counterfactual selection bias, or hiding latency and costs.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: