Define the job before choosing the model
Be explicit about the task an AI system supports, the decisions it must not make, and who remains accountable. In healthcare, an administrative assistant and a system that influences clinical decisions have different risks and review needs.
Give people meaningful control
A human review step should let a person inspect the source, understand uncertainty, correct the result, and stop the workflow. A button marked approve is not enough if the reviewer cannot evaluate what they are approving.
Respect information boundaries
Agree what information is needed, where it can be processed, who can access it, and how it is retained. Review the complete chain, including model providers, application logs, connected tools, and support access. Regulatory and contractual requirements need review by the organization’s qualified privacy, security, and legal stakeholders.
Evaluate before and after launch
- Use representative, appropriately authorized evaluation data.
- Test incorrect, incomplete, ambiguous, and out-of-scope inputs.
- Document acceptable performance and escalation criteria.
- Monitor failures and reassess when data, models, or workflows change.
Responsible AI is an operating practice, not a badge added at the end of a project.
For a broader risk-management reference, see the NIST AI Risk Management Framework. Our implementation planning adapts the questions to the specific use case; it does not imply a certification or guarantee of compliance.