Make it clear who will review a concern and how an error can be corrected
An employee who believes a performance assessment has missed important work needs a way to raise the issue and have the evidence considered. If AI helped prepare the assessment, the review also needs to establish what the system contributed and whether that contribution was accurate.
That is a practical test of an organization's AI policy. Naming a human decision-maker is a start. The employee also needs to know where to take a concern, what information to provide, and who has the authority to correct an error.
Build that route into the existing HR process before expanding the tool's role. The process should work for someone who understands the disputed outcome but knows little about the technology behind it.
Start with the decision the employee wants reviewed. A person should not have to prove that an algorithm caused the problem before HR will examine it. The concern may involve an inaccurate record, missing context, an inappropriate criterion, or the way a manager interpreted a recommendation..
“A person should not have to prove that an algorithm caused the problem before HR will examine it.”
Explain where to submit the concern, what happens next, and when the employee should expect an acknowledgment and an update. Ask for enough information to identify the decision and the issue without requiring a technical account of the model.
Where an employee disputes a manager's decision, provide an escalation route beyond that manager. Define who receives the concern and how it moves to someone with the necessary authority. An inbox without that responsibility creates a contact point but not a review process.
Consider a performance summary that gives little weight to a major project. The reviewer needs to establish whether the project was absent from the source material, omitted by the tool, or considered and given less weight by the manager. Each explanation calls for a different response.
Preserve the relevant input, recommendation, and decision record under the organization's retention policy. Let the employee supply missing or corrected information, and compare that information with the criteria used in the original decision.
“The reviewer should be able to examine the substance without treating the original model output as the default answer.”
The reviewer should be able to examine the substance without treating the original model output as the default answer. Running the same material through the same tool again may reproduce the disputed omission. Where specialist advice is needed, identify who can provide it and who remains responsible for reaching a conclusion.
Tell the employee what issue was examined, what evidence was considered, and whether the decision changed. If it did not change, explain the relevant grounds. Keep the explanation specific enough for the person to understand how the concern was addressed.
The communication should also identify the next available step if the employee believes a material issue remains unresolved. Share information appropriate to the case while protecting other employees' records and legitimate confidentiality requirements.
NIST's voluntary AI Risk Management Framework includes mechanisms for appeal and override within post-deployment monitoring. Its Playbook also recommends ways for affected people to contest problematic outcomes. That guidance supports treating employee concerns as part of operating and evaluating an AI-supported process. The organization's procedure should also account for any legal, contractual, or collective-bargaining requirements that apply to the particular decision.
Resolving a complaint may require more than changing the explanation. If a source record was wrong, identify where it needs to be corrected. If the error affected a rating, compensation recommendation, or later decision, determine which outcomes need to be reconsidered and who will do that work.
Confirm that the agreed correction reached the relevant systems and people. Where the tool or its configuration caused the problem, assign an owner to investigate whether similar cases may be affected and whether use should be limited while the issue is examined.
Give the employee a clear account of the action taken and any remaining work. Avoid closing the case simply because the concern has been forwarded to a vendor or another internal team.
Review recurring issues across cases without assuming every complaint proves a system defect. Patterns can indicate incomplete source records, unclear criteria, weak explanations, or a problem with the tool. They can also reveal that employees do not understand how a decision was reached.
Track acknowledgment and resolution times, the reasons outcomes changed, and whether agreed corrections were completed. A low complaint count should prompt a question about accessibility as well as satisfaction: employees may be unaware of the route or uncertain that using it will help.
“A low complaint count should prompt a question about accessibility as well as satisfaction.”
Test the process with a realistic disputed decision before wider rollout. Follow the concern from submission to explanation and correction. That exercise shows whether the named reviewer has access to the evidence, enough time to assess it, and authority to act.
Connex convenes CHROs and senior HR leaders in Trusted Peer Groups. Ask peers how employees raise concerns about AI-influenced decisions, who reviews them, and what the organization has changed after a concern exposed an error.