Filed July 13, 2026 in the Northern District of California (No. 3:26-cv-07122-WHO) as a "Complaint for Injunctive and Declaratory Relief in Aid of Arbitration and for Damages," filed concurrently with a preliminary-injunction motion and a TRO application. See Former Meta Employees v. Meta (AI-assisted layoffs).
The alleged mechanism
The complaint's factual core is a claim about how the layoff list was built. On May 20, 2026, Meta "began notifying approximately ten percent of its workforce that they had been selected for termination in a mass reduction in force."
The allegation: "Meta did not assemble the termination list through the considered judgment of managers who knew the work. Instead, Meta used a constellation of internal artificial-intelligence systems — including a system referred to internally as 'Metamate,' employee-trained 'second-brain' agents, keystroke- and activity-monitoring data, AI-token-usage dashboards, and algorithmically assisted performance ranking and calibration — to score, rank, and select employees for inclusion on the list."
The discrimination theory
The legal theory does not allege intentional targeting. It alleges that the inputs are structurally incompatible with protected leave.
Those tools "draw on inputs — performance ratings, calibration scores, productivity and output metrics, 'AI-native' ratings, and AI-token consumption — that, by design, cannot be accumulated by an employee who is on protected medical or family leave, or whose output is reduced by a disability."
Three omissions are alleged on information and belief: that Meta "did not neutralize those inputs for protected leave"; "did not exclude protected-leave-takers or accommodation-seekers from the selection cohort"; and "did not pause the system for the individualized, leave- and accommodation-neutral review that the law requires."
The claimed result: employees who took protected leave "were disproportionately selected for layoff, based on scoring that not only failed to account for their protected leaves, but in effect penalized the employees for exercising their legal rights to these leaves."
The AI-token-consumption metric is the most novel element — a productivity proxy that measures use of the employer's own AI tools, and that necessarily reads as zero for anyone not working.
Plaintiffs
Twenty-six current and former employees, each selected in the RIF and each of whom, within the preceding twenty-four months, "took, requested, or was approved to take statutorily protected leave; attempted to take protected leave and suffered interference; or requested or received a reasonable accommodation for a disability." Their accounts span California, Illinois, Washington, New York, the District of Columbia, Pennsylvania, and Florida.
Two are described in the complaint's opening: "A scientist was selected while on approved pre-birth pregnancy leave — the day before her water broke, and just two days before she gave birth." And a manager "whose own performance review documents that his demotion" followed protected leave.
Standing of these allegations
This is a complaint: the allegations are unproven, and Meta's response is not in this document. What it establishes for the record is the theory being tested — that algorithmic selection can produce disparate impact on protected classes through facially neutral productivity metrics, without any discriminatory intent, and that an employer's duty of individualized review is not discharged by an automated ranking.
Relationships
- supports: Former Meta Employees v. Meta (AI-assisted layoffs) — the primary text behind that page
- related: Mobley v. Workday, Inc. — the other leading algorithmic-employment-discrimination case
- related: AI Labor Disruption, AI and Tort Liability, AI and Civil Liberties, Meta AI