AI Assurance, Validation, and Performance Testing

Module 1: assurance planning

Establish the vocabulary, boundaries, stakeholders, and decision context for assurance planning. Use evidence review, responsibility mapping, and implementation workshops to test assumptions against the client context.

Separate established requirements and reliable evidence from untested assumptions about assurance planning. Record the decision, supporting evidence, and unresolved questions in the governance diagnostic.

Map assurance planning to the organization’s current responsibilities, dependencies, and constraints. State what would trigger rejection, escalation, or redesign of the proposed approach.

Module 2: test dataset design

Diagnose the current state of test dataset design using a structured evidence review. Use evidence review, responsibility mapping, and implementation workshops to test assumptions against the client context.

Compare alternative methods and select an approach suited to risk, maturity, and scale. Record the decision, supporting evidence, and unresolved questions in the evidence review.

Document requirements, owners, decision criteria, and exceptions for test dataset design. State what would trigger rejection, escalation, or redesign of the proposed approach.

Module 3: quality fairness and robustness

Design the workflow, safeguards, and handoffs required for quality fairness and robustness. Use evidence review, responsibility mapping, and implementation workshops to test assumptions against the client context.

Test normal, failure, and edge-case scenarios before operational adoption. Record the decision, supporting evidence, and unresolved questions in the implementation roadmap.

Review the design for security, quality, accessibility, sustainability, or assurance implications as relevant. State what would trigger rejection, escalation, or redesign of the proposed approach.

Module 4: acceptance evidence

Define meaningful measures, evidence, review cadence, and escalation thresholds for acceptance evidence. Use evidence review, responsibility mapping, and implementation workshops to test assumptions against the client context.

Investigate performance gaps and separate root causes from symptoms. Record the decision, supporting evidence, and unresolved questions in the governance diagnostic.

Plan corrective action, controlled change, and accountable follow-through. State what would trigger rejection, escalation, or redesign of the proposed approach.

Module 5: Applied AI Assurance, Validation, and Performance Testing Workshop

Complete a evidence review that integrates the course decisions around validation review board. Use evidence review, responsibility mapping, and implementation workshops to test assumptions against the client context.

Defend recommendations against a realistic stakeholder challenge or scenario. Record the decision, supporting evidence, and unresolved questions in the evidence review.

Produce a prioritized workplace action plan with owners, dependencies, and review points. State what would trigger rejection, escalation, or redesign of the proposed approach.