When employers use AI tools to help decide who is selected for termination, the risk is not always an obviously flawed tool. The more practical risk is that the tool may appear objective while missing important context HR and leadership need before turning a recommendation into a final decision.

That is where the “rubber stamp” trap starts.

Most employers have heard the phrase “garbage in, garbage out” when discussing AI. But in this context, the issue is not always bad data. A tool may rely on information that is accurate, familiar, and business-relevant, yet still produce an incomplete result.

The practical takeaway is straightforward: AI-generated separation recommendations may be  part of a review process. That review process is most meaningful when it tests a recommendation rather than simply approving it. Employers that rely on those recommendations without testing the inputs, outputs, and surrounding context may create avoidable risk, particularly if a neutral metric produces results that are difficult to explain or defend later.

When Accurate Data Tells an Incomplete Story

Performance ratings, productivity metrics, utilization data, project output, attendance records, and similar workforce measures are familiar business metrics which employers have relied on for years. AI changes the scale and speed at which those metrics can be processed, weighted, and converted into recommendations. That efficiency can be valuable, but it also raises the stakes: before using the output to support a separation decision, employers may want to consider whether the data tells the whole story.

The challenge is that even accurate data may not capture the full context surrounding an employee’s performance or contribution. A 12-month productivity metric may look different if one employee was available to work for the full year and another was on approved leave for part of that period. Utilization data may likewise appear lower for an employee working under an approved reduced schedule, even if the employee is meeting expectations. In each situation, the data may be accurate while still telling an incomplete story.

That distinction matters because AI systems are designed to identify patterns and rank information based on the data they receive. Unless a tool is built or configured to account for differences such as approved leave or a reduced schedule, its ranking will not reflect them. For that reason, employers may want to be cautious about assuming that a recommendation is sound simply because it was generated from objective business data.

Lawmakers are focused on this issue as well. On September 30, 2026, California Governor Gavin Newsom signed SB 947, the “No Robo Bosses Act,” which, beginning July 1, 2027, bars employers from relying solely on an automated decision system to make discipline or termination decisions and requires human review and corroboration when such a system is primarily relied upon. For employers using AI in separation decisions, the new law reinforces why the review step matters.

Where Individual Circumstances Fit

Individualized review allows decision-makers to consider whether a particular employee’s circumstances affected how a metric measured that employee’s work. Whether and how that context should affect the analysis will depend on the criteria, the facts, the decision, and the applicable law. There is no single adjustment that fits every situation, and individual circumstances do not automatically override legitimate, job-related criteria.

The goal is to confirm that the criteria are tied to the work, applied consistently, and producing results that make sense in context. When individual circumstances are considered, doing so through a consistent process with a common standard can help show that similarly situated employees were treated alike and that the final decision rested on business reasons.

Questions to Consider During Review

The right approach will depend on the tool, the decision being made, and the applicable law. Before finalizing an AI-assisted separation decision, employers may want to consider questions such as:

  • Inputs. What data is the system analyzing, and does it accurately reflect the performance or business factors the organization intends to measure?
  • Outputs. Do the recommendations align with business objectives? Do any unexpected patterns or outcomes warrant closer review?
  • Criteria. Are the metrics job-related, tied to legitimate business needs, and applied consistently across the workforce?
  • Context. Could circumstances such as approved leave or a reduced schedule have affected an employee’s opportunity to generate the measured output?
  • Authority. Who has the information and authority to pause, reject, or escalate an AI-assisted recommendation before the decision becomes final?
  • Process and documentation. Is there a consistent process for evaluating recommendations, considering individual circumstances, and recording the rationale for final decisions?
  • Role of counsel. At what stages would legal review be most useful, such as when the process is designed or before decisions are finalized?

Avoiding the Rubber Stamp Trap

AI can help employers sort and analyze workforce information, but it is not a substitute for judgment. In the separation context, employers may need to explain not only what the tool recommended, but why the final decision was consistent with business objectives, how it was applied through a structured process, and whether it was supported by the facts.