NEWS & ANALYSIS | WORK & POWER
Twenty-six Meta employees say AI-assisted systems helped put them on a layoff list after they took protected medical, parental or family leave, or received disability accommodation. Meta says the claim is wrong and that people made the decisions. A federal judge has refused to stop the layoffs, but the court has not decided whether the workers’ allegations are true.
By Andrew McDonald · Immortal AI · Part One
For years, one of the simplest promises made about artificial intelligence in the workplace has been that a person would remain responsible for important decisions.
That distinction is now being tested in a case involving 26 Meta employees who say the formal decision may have belonged to people, but the information shaping that decision came from systems they could not see, challenge or properly interrogate.
The workers filed a lawsuit in California alleging that Meta used a collection of internal AI and algorithmically assisted systems to score, rank and help select employees during a workforce reduction affecting about 8,000 jobs.
Meta denies it.
“Workforce management and organizational decisions were and are made by people, not AI,” the company said in response to the claims.
That leaves a question that matters well beyond Meta.
When is a person’s decision really human?
What the workers allege
The 26 plaintiffs say they had taken protected medical, parental, pregnancy, caregiving or family leave, or had requested or received disability accommodation.
Their complaint alleges that Meta’s layoff process relied on a “constellation” of systems and signals, including internal AI tools, keystroke and activity-monitoring information, AI-token-usage dashboards and algorithmically assisted performance rankings.
The allegation is not simply that a machine produced a list and automatically fired people.
It is that the systems used to evaluate activity and productivity could disadvantage workers whose legitimate absence from work meant they had fewer opportunities to generate the signals being measured.
According to the complaint, those scores and ratings could not be accumulated in the same way by someone who was on protected leave or whose output was reduced by a disability. The workers say Meta failed to neutralise those absences before the information flowed into the layoff process.
Those allegations have not been proven.
Meta says the premise is false
Meta says the lawsuit lacks merit and is not based on facts.
Its position is direct: people, rather than AI, made workforce and organisational decisions.
That denial matters. It would be wrong to report the employees’ description of the system as an established account of what happened inside Meta.
The public record currently contains competing claims. The employees describe AI-assisted and algorithmic systems feeding a selection process. Meta says AI did not make the decisions.
The unresolved issue sits between those positions.
A manager can technically approve a decision while relying heavily on a score, ranking or recommendation produced elsewhere. Whether that amounts to meaningful independent judgement depends on how the system was used, what information the manager saw, what discretion existed and whether the underlying data could be challenged.
The judge did not decide who was right
The employees sought emergency court intervention to stop their separations while the underlying claims proceed through private arbitration.
US District Judge William Orrick refused to issue that temporary restraining order.
That was not a finding that Meta had disproved the allegations.
The judge concluded that the workers had not met the legal threshold needed for the emergency relief they were seeking. Reuters reported that he nevertheless said the plaintiffs had raised serious questions concerning the alleged use of AI in the layoff process and left open the possibility of reconsidering temporary relief if stronger evidence emerges.
This distinction is important because a failed application for an emergency order can easily be misread as a failed case.
It is not.
The central factual dispute remains unresolved.
The evidence problem may be the bigger story
The case exposes a structural problem for workers challenging algorithmically influenced employment decisions.
The company generally controls the system.
It knows which data were collected, how rankings were generated, what weighting was used, what managers saw, whether a recommendation could be overridden and how much influence each tool had on the final outcome.
The employee sees the result.
Reuters reported that legal experts see proof as a central difficulty in the Meta case, particularly because much of the underlying evidence is internal and the workers’ disputes are moving toward private arbitration.
This creates an accountability problem even when a person remains formally responsible for the final decision.
If an organisation can say “a person decided” without explaining what information shaped that person’s judgement, the human decision-maker can become a shield around an automated process rather than a safeguard against it.
Protected leave makes the allegation more serious
The workers’ claim has another layer.
Time away from work for pregnancy, parental responsibilities, medical treatment or disability can reduce activity measures for obvious reasons. If those measures are later treated as evidence of weaker performance without being adjusted for protected absence, the system can reproduce discrimination without ever being instructed to discriminate.
That does not establish that Meta’s system did so. It explains why the allegation deserves scrutiny.
A model does not need a field labelled “pregnancy” or “disability” to create unequal outcomes. A proxy such as logged activity, output volume or tool use may correlate with circumstances the law protects.
This is one reason accountability cannot stop at asking whether an AI explicitly made the final decision.
When is a person’s decision really human?
The phrase “human in the loop” has become a reassuring shorthand in discussions about artificial intelligence.
But a person clicking approve at the end of a process does not automatically make that process meaningfully human.
The real questions are harder.
Did the person understand how the ranking was produced? Could they see the relevant limitations? Were protected absences removed from the calculation? Could the worker challenge incorrect data? Was the manager expected to depart from the recommendation? Did doing so carry a cost?
If the answers are unknown, saying a person made the decision tells us less than it appears to.
That is the significance of the Meta case even before the allegations are resolved.
It forces a distinction between human approval and human judgement.
What we know, and what we do not
We know that 26 Meta employees filed the case. We know the complaint alleges that internal AI and algorithmically assisted systems contributed to the layoff-selection process. We know every plaintiff had taken protected leave or sought or received disability accommodation. We know Meta rejects the allegations and says people made the decisions. We know the judge refused the workers’ request for an emergency order stopping the layoffs.
We do not yet know precisely how Meta’s internal systems were weighted in the final selections, whether the plaintiffs’ account of the technology will be supported by internal records, or whether the alleged process unlawfully disadvantaged people who took protected leave.
Those questions require evidence that has not yet been fully tested in public.
That uncertainty is not a reason to dismiss the story.
It is the story.
Accountability cannot disappear between the model and the manager
AI does not need authority to fire someone in order to influence who gets fired.
A system that scores, ranks, filters or recommends can shape the range of decisions a manager believes are reasonable. The more complex and opaque the process becomes, the easier it is for responsibility to become fragmented.
The developer can say the model only provided information. The manager can say they relied on the company’s systems. The company can say a person made the final decision.
The employee is still unemployed.
That is why the standard cannot simply be whether a person appeared somewhere in the chain.
It has to be whether someone had enough knowledge, authority and responsibility to recognise a bad outcome and stop it.
Continue this investigation: AI Rejected You. Who Is to Blame? examines accountability when automated systems influence workplace and institutional decisions.
Coming next: Part Two
Part Two will follow when the next material evidence emerges. We will examine what new filings reveal about how Meta’s systems actually worked, what managers were shown, and whether the distinction between an AI-assisted recommendation and a human decision survives closer scrutiny.
Editorial note: The employees’ claims are allegations and have not been proven. Meta denies that AI made the layoff decisions. The court’s refusal to grant emergency relief did not resolve the merits of the underlying allegations.
Editorial disclosure: Immortal AI uses AI-assisted research and drafting. Material claims in this article were checked against the complaint as described in independent reporting, Meta’s response and reporting on the court ruling. Final editorial decisions remain the responsibility of Immortal AI.
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