What this article covers
An AI policy becomes useful when it defines who can approve a use case, what evidence is required and what happens when the system fails. The level of control should reflect the consequences for people and the organization.
1. Define the decision being influenced
Describe the task and who will be affected. A tool that drafts internal communications has different risks from a system influencing hiring, credit or essential services. Identify data, users, the supplier, integrations and prohibited uses.
Document the alternative without AI as well. If the benefit cannot be assessed or errors cannot be handled, reducing the scope or postponing adoption may be the appropriate decision.
2. Assign responsibility and authority
The NIST AI RMF organizes risk management through Govern, Map, Measure and Manage. It is a voluntary reference, not certification or automatic proof of compliance.
In practice, name the owners of the business use, technical evaluation and impact response. Define who can stop the system and how a model or supplier change returns to assessment.
3. Evaluate results and unequal effects
Build examples representing actual use, including less common cases. Analyze errors by relevant context so that averages do not conceal concentrated failures. Collecting evaluation data also needs justification and protection.
- Record limitations and conditions in which results must not be used.
- Check whether explanations allow conclusions to be verified.
- Test human review with sufficient time and information.
- Define approval criteria before examining the results.
4. Provide a way to challenge decisions
People affected by a decision need to understand how to request clarification or review through the appropriate channel. Avoid assigning responsibility to a vague claim that “the algorithm decided.” The responsible team must be able to investigate inputs, versions and outcomes while protecting privacy.
Human review that exists only on paper does not solve the problem. Reviewers need competence, evidence and authority to change or suspend a decision.
5. Monitor after approval
Keep a record of incidents, complaints and performance changes. Agree on a review frequency and exceptional triggers, such as a change in the population served. Record the decision to continue, restrict or retire the system.
The AI checklist supports that discussion. Explore Cortex for the product’s approach; governance of each use case still requires decisions by the organization.