Meta Faces Lawsuit Over AI-Driven Layoff Decisions Impacting Employees on Leave
Meta is being sued by 26 employees who claim the company used AI-based systems to evaluate workers and select layoffs without properly accounting for absences due to medical or family leave. The lawsuit, filed in the federal court in Oakland, California, follows Meta's mass layoffs announced in May. Plaintiffs allege that while management presented the layoffs as human decisions, AI tools analyzed productivity, code changes, performance reviews, AI tool usage, and computer activity to rank employees, disadvantaging those on approved leaves such as maternity, medical, or family care.
According to the complaint, employees who were on leave showed reduced activity and lower AI tool usage, resulting in poorer scores compared to active workers, effectively penalizing legally protected absences. About half of the plaintiffs were on leave for pregnancy, childbirth, parental care, or family medical reasons. One employee reportedly received a layoff notice less than two weeks after returning from six months of maternity leave. Another was laid off during medical leave despite assurances it would not affect performance evaluations. In both cases, the only team members laid off were those on leave.
The plaintiffs do not accuse Meta of intentionally discriminating but argue that relying solely on activity metrics without context can produce unintended bias against protected groups. Meta denies the allegations, stating that human managers made all final decisions and AI systems did not autonomously determine layoffs. The dispute centers on how much AI-generated data influenced those decisions. The employees seek to temporarily halt their terminations pending legal or arbitration proceedings, citing risks of losing salary, health insurance, unvested stock, and work visas.
The lawsuit remains in early stages, and the claims have yet to be proven. Meta continues to defend its layoff process amid growing scrutiny of AI's role in workforce management.
The same event, reported separately by each outlet. Open a few to compare what different newsrooms emphasize — and what they leave out.
Not the same event — other stories that share this one’s people, places, or theme: background, reactions, and follow-ups.