What it means
Symbolic AI represents knowledge and rules explicitly, while statistical AI learns patterns from examples and probability distributions.
Why it matters
The distinction affects explainability, data dependence, failure modes, testing, and the controls needed when both approaches are combined.
Practical focus
- Define the approved purpose, accountable owner, users, operating environment, and acceptable risk for symbolic and statistical ai before implementation or procurement
- Implement an architecture decision that records where rules, learned components, and human judgment each control behavior through documented architecture, policy, configuration, and change control
- Collect rule inventories, training-data records, model documentation, and tests of conflicts between learned outputs and explicit rules and compare the results with approved requirements, risk thresholds, and expected outcomes
- Exercise this failure scenario as a planned test: a learned component overrides a mandatory rule or a brittle rule set fails outside its encoded cases
Common mistakes
- Using the label symbolic and statistical ai without defining the exact system boundary, decision, data, and people in scope
- Treating a model response or vendor claim as sufficient evidence without testing the surrounding application and operating process
- Failing to preserve rule inventories, training-data records, model documentation, and tests of conflicts between learned outputs and explicit rules in a form that lets reviewers reconstruct decisions and changes
- Testing only the normal path and never rehearsing what happens when a learned component overrides a mandatory rule or a brittle rule set fails outside its encoded cases
Certification relevance
This subject appears in or supports the following certification bodies of knowledge: