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Leadership guide/Decisions/Monitoring & policy renewal
A policy can remain stable while the systems, uses and responsibilities beneath it change.
HumanSkills recommendation · September 2026
In this guideRecommended positionYour local choicesA campus situationWho is responsibleLanguage to useEvidence of implementationSupporting sourcesDownload PDF→
Read, share and use with your team.
HumanSkills recommendationSet regular review and event-based triggers. Evaluate whether benefits, safeguards and human support remain effective. Time-limit pilots and exceptions, and record a decision to continue, change, restrict or retire the use.
An annual review alone can miss a consequential midyear feature change. A pilot can become permanent through inaction. Monitoring should examine the intended outcomes and difficult cases without creating unnecessary surveillance.
What the evidence supportsCarteret and RPCC call for policy review. NIST provides a voluntary framework covering AI use and evaluation. Our recommendation combines periodic review with specific triggers and accountable continuation decisions.
The right arrangement depends on the purpose, consequences, applicable requirements and capacity of your institution. Use these options to make the choice explicit.
Routine review
Consider this when: The use is stable and monitoring provides adequate assurance.
The tradeoff: Match frequency and depth to consequences; do not substitute a date for substantive review.
Triggered reassessment
Consider this when: The purpose, data, population, vendor or ability to act changes materially.
The tradeoff: Make triggers understandable and assign someone to identify and escalate them.
Retire or replace
Consider this when: Benefits are not demonstrated or safeguards cannot be maintained.
The tradeoff: Plan continuity, information disposition, contracts and communication with affected people.
A department’s pilot has passed its review date. Staff rely on it, the vendor has changed features and nobody has made a continuation decision.
Usefulness does not remove the need for an accountable decision about the changed service.
What the team produces: A continuation, restriction or retirement decision supported by current evidence.
Board & trustees
Ask whether leadership can account for significant deployments, unresolved issues and changes over time.
Institutional leaders
Assign ongoing ownership after the task force’s initial work is complete.
AI task force
Design the handover, review triggers and reporting arrangements for standing governance.
Apply these roles within your institution’s actual governance and delegated authority.
Illustrative model clause
AI deployments, pilots and exceptions must have an accountable owner and defined review conditions. Material changes require reassessment. Continued use must be supported by appropriate evidence of benefit, effective safeguards and available human support. Expired pilots and exceptions do not renew automatically.
Copy the clause
Review this clause with the full policy and local requirements. It is an implementation starting point, not a statement of measured consensus.
Model policy §4→Model policy §11→Model policy §12→
When to reconsiderTrigger reassessment for new capabilities, data, populations, terms, incidents, complaints or loss of responsible capacity.
Selected precedents and guidance supporting this chapter. These sources do not imply institutional endorsement of HumanSkills or agreement with every recommendation.
Source review: 24 September 2026. Read the editorial approach.
Continue the workStart with the purpose.
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Read the research comparisons and explore the annotated library across US colleges, universities and international institutions. Use the model language as a starting point for your own decision.
Assign ownership and keep the policy current.→Make the policy yours→