Signals vary in reliability and age. Preserve their source, freshness and business impact when combining them; an unexplained score is insufficient.
Evaluation approach
Classify signals by source, freshness and error probability. Local observations, platform evidence and server behavior have different trust assumptions.
Application example
Do not count three root indicators derived from the same system feature as three independent pieces of evidence.
Limits and considerations
More signals do not automatically improve accuracy. Model missing information separately.
Avoid counting the same evidence repeatedly
Detections sharing a system characteristic should not be summed as independent evidence. Understand each signal's origin, age and failure conditions. A numerical score does not remove uncertainty.
Assess policy effects using normal users and verified incidents. Retain missing signals as a distinct state. A new field can alter policy balance, so collecting more data is not itself success.
Checks and decisions
- Map signal dependencies
- Retain freshness
- Expose missing data
Calibrate policies with representative user data and controlled tests. A score is a model, not unquestionable truth.
Sources
The primary references above provide the technical basis. Example workflows and evaluation suggestions are this publication’s explanations, not independent test results for a particular product.