End-to-end inference path tests exercise the complete chain from data ingestion through feature engineering, model inference, post-processing, explanation generation, and output delivery. A curated test dataset with known expected outcomes is submitted to the system’s external interface. The tests validate the system’s end-to-end accuracy, latency, and output format.

The most compliance-critical aspect of end-to-end testing is the verification that the correct log entries were created at each stage. A test that confirms the correct output was produced but does not verify that the logging layer captured the inference event, the feature values, the model version, and the post-processing decisions is incomplete from an Article 12 perspective. End-to-end tests should therefore validate both the output and the audit trail.

The test dataset should include cases that exercise each branch of the post-processing logic, each explanation type, and each output format. It should also include cases from each protected characteristic subgroup, ensuring that the end-to-end pipeline produces correct results for all subgroups. The test dataset is version-controlled and expanded over time as new edge cases are discovered through production operation.

Key outputs

• Curated test dataset with known expected outcomes per subgroup
• End-to-end tests validating output correctness and audit trail completeness
• Latency and format validation
• Module 5 AISDP evidence