Enterprises that use AI in hiring and firing decisions continue to be under scrutiny, and this time it’s Meta under the microscope.
A legal complaint filed on July 13 in a US District Court in California alleges that Meta used AI systems that unfairly and illegally selected workers for termination while they were out on protected leave.
More than two dozen anonymous plaintiffs are seeking a preliminary injunction that would prevent the company from finalizing their separations or altering their compensation, benefits, or protected leave status.
Meta has countered that the claims lack merit and that its workforce decisions were, and continue to be, made by people, not AI.
An important lesson
These allegations should serve as an important lesson to other businesses using AI in their HR decision-making, analysts note.
“Enterprises must begin by rejecting the convenient assumption that AI improves workforce decisions simply by touching them,” said Sanchit Vir Gogia, chief analyst at Greyhound Research.
There is “scant independent proof” that AI makes layoff choices more accurate or more lawful, he said. “It makes them faster, and faster has never been shown to be fairer.”
The claims against Meta
The complaint states that, on May 20, 2026, Meta began notifying roughly 10% of its workforce (around 8,000 employees) that they had been selected for termination. The company also announced that several thousand more would be reassigned to new AI initiatives. But this came even as Meta reported record revenues in Q1 2026 ($56.31 billion, a 33% year-over-year increase), and pledged to spend upwards of $100 billion on AI this year.
In addition to questioning the need for staff cuts, the filing alleges that Meta used a “constellation” of internal AI systems to score, rank, and select employees for termination. These tools included Meta’s internal AI coworker, “Metamate,” employee-trained “second-brain” agents that replicated their output, algorithms tracking keystrokes and other digital activity, and AI token usage dashboards.
“Meta did not assemble the termination list through the considered judgment of managers who knew the work,” the complaint claims.
The 26 plaintiffs, all current or former employees, requested, took, or were approved for “statutorily protected” leave within 24 months of the workforce reduction, and claim they were “disproportionately selected” for layoff based on scoring that essentially penalized them for exercising their legal right to take leave.
These practices are prohibited by federal and state law; The US Family and Medical Leave Act, for one, prohibits the use of protected leave as a “negative factor” in employment decisions. Further, the plaintiffs allege that Meta violated the US Worker Adjustment and Retraining Notification (WARN) Act that requires employers with 100 or more employees to provide written notice 60 calendar days in advance of mass layoffs.
This notice gives employees reasonable time to seek alternate employment; however, the complaint argues, an employee undergoing “significant medical treatment” or providing “around the clock care” for a “weeks old newborn” or other loved ones “cannot also be told that during this exact same time period they must look for new work.”
In one scenario, according to the filing, a scientist was identified for termination just two days before she gave birth while on pregnancy leave. In another, an engineer’s manager tied his performance rating to “broken time” when an injury prevented him from working. In a third, a researcher was called out after requesting time off following a medical diagnosis.
The plaintiffs are seeking a preliminary injunction pending an independent audit of the “algorithmically assisted selection process” and “resolution of the merits of their claims” in arbitration.
Once terminations are finalized, the harm to plaintiffs “cannot be undone by money damages alone,” the complaint states. For employees out on leave, “every day that goes by constitutes additional harm, in that Meta is taking away the entire purpose of a protected leave.”
Considerations for enterprises
Any system that materially influences who keeps a job is not an HR tool, Gogia noted. “It is high-risk enterprise infrastructure.”
An “AI-determined” process delegates the outcome to the system, while an “AI-assisted” one gives the system the ability to rank, recommend, and summarize, with a human formally making the final decision. Exposure arises in either model, Gogia pointed out, because the output has often been compressed and eliminates detail by the time of executive approval.
There must be one non-negotiable role in the process, Gogia said: A single executive with the authority to halt the process, suspend the model, and delay decisions when evidence does not hold. This person should be “a meaningful reviewer [who] understands the model’s limits, knows the actual work, and holds the authority to challenge the recommendation, with every override visible and reviewable,” he said. At the same time, the objective is to “govern the machine and the manager together,” since human judgement brings its own “risks, favoritism, and proximity” bias.
Gogia advised enterprises to retain fixed memory for auditing, determine who chose the auditor, what was excluded, and whether the result can be reproduced. They should also inventory every source feeding the model and its origins, and run adverse-impact analysis before making any firing decisions.
Leave details must never be identified as inactivity or weak adoption; a protected absence is not ordinary missing data, and the system has to be informed of this. Rather, these circumstances belong in an “independent review lane,” where human reviewers get enough context to “neutralize” the period without receiving specific leave details, Gogia said.
He pointed to another important question: What should the “second brain” AI agent that ingested the employee’s communications and documents to replicate the employee’s output be allowed to do when humans are away, and who owns that output?
Ultimately, said Gogia, “the safest position is not to ban AI from workforce planning. Used with discipline, it can expose duplicated work and inconsistent assessment, and it can challenge human bias rather than automate it.”
How employees can protect their rights
Employees, for their part, need a genuine window in which to challenge inaccurate data before separation becomes “irreversible,” and they should “fight the record, not the algorithm,” Gogia advised.
That means that, while the model cannot explain itself, documented evidence can. Employees should lawfully retain their own reviews, leave approvals, and severance documents, and build a chronology of events: When leave was requested, when performance language changed, when new metrics appeared, Gogia said.
Impacted workers should ask in writing which criteria were used in the decision, whether automated systems materially influenced it, how protected leave was treated, and what information about them influenced the result and how that information was verified.
Further, it’s important to take note of deadlines; the federal discrimination window is typically six months, although that is extended to 10 in many places, and internal processes are “not obliged to respect it,” said Gogia.
His ultimate advice for workers: “Preserve the lawful record, and protect the deadline.”
This article originally appeared on CIO.com.