Assurance / Responsible AI
Decide where AI belongs before deciding how to build it.
We define the purpose, decision boundary, affected users, oversight model, and evidence requirements before an operational workflow is deployed.
Questions that shape the implementation.
Responsible AI work changes the workflow design. It determines which data is permitted, where review occurs, what is evaluated, and how people can intervene.
- Purpose
- State the service task, intended user, affected people, and expected benefit.
- Boundary
- Separate recommendations, drafts, low-risk actions, and decisions that must remain human.
- Oversight
- Assign review authority, escalation paths, fallback procedures, and stop conditions.
- Evidence
- Define the tests, records, documentation, and review cadence needed for the use case.
- Access
- Limit data and system permissions to what the approved workflow requires.
- Adoption
- Prepare operators and reviewers to interpret outputs, handle exceptions, and challenge the system.
Artifacts the client can use after the engagement.
- Purpose and boundary statement
- Who the workflow serves, what it does, what it must not do, and where human authority begins.
- Controls map
- Roles, approvals, access levels, escalation logic, audit fields, and operating limits.
- Impact-readiness record
- Risk questions, affected groups, testing evidence, traceability notes, and unresolved decisions.
- Operations handover
- Reviewer procedures, administrative documentation, training material, monitoring cadence, and support ownership.
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