A group of 26 former Meta employees has filed a lawsuit in US District Court for the Northern District of California, accusing the technology giant of relying on artificial intelligence systems to select workers for termination during a round of layoffs that eliminated 8,000 positions. The plaintiffs, identified only as "Doe" in court filings, contend that Meta bypassed human managerial judgment in favor of algorithmic decision-making when determining who would lose their job.
AI Systems Allegedly Drove Termination Decisions
According to the complaint, Meta did not assemble its termination list through the considered judgment of managers familiar with employees' actual work. Instead, the lawsuit alleges, the company deployed a network of internal AI systems to score, rank, and select individuals for dismissal. Among the tools cited in the filing are a system known internally as "Metamate," employee-trained "second-brain" agents, keystroke and activity monitoring data, AI-token-usage dashboards, and algorithmically assisted performance ranking and calibration systems.
The complaint further claims that Meta evaluated employees partly based on their engagement with the company's own artificial intelligence tools. Internal dashboards reportedly classified workers according to their stage of AI adoption, using labels such as "AI Native," "AI First," and "AI Enabled." Workers who did not sufficiently adopt these tools may have been penalized in the ranking process as a result, the filing suggests.
Disparate Impact on Workers With Disabilities and Protected Leave
A central allegation in the complaint is that Meta's AI-driven evaluation tools failed to account for differences caused by disabilities and legally protected medical or family leaves. The lawsuit states that the inputs used to rank employees — including performance ratings, calibration scores, productivity and output metrics, "AI-native" ratings, and AI-token consumption — cannot reasonably be accumulated by an employee who is on protected medical or family leave, or whose output is reduced by a disability.
The plaintiffs argue that this systemic blind spot meant that workers with disabilities and those who took protected leaves were disproportionately targeted for termination. By design, the complaint asserts, the algorithmic inputs penalized employees whose circumstances naturally reduced their measurable output or AI tool usage, creating a discriminatory effect even if the intent was neutral.