Meta is currently embroiled in a legal battle following accusations from numerous employees who argue that the tech giant utilized artificial intelligence to pinpoint staff for layoffs, which allegedly disproportionately targeted those on maternity leave, medical leave, or with disabilities. The lawsuit, lodged in a California federal court, contends that Meta employed AI-driven evaluations and productivity data to determine which employees would be let go as part of a significant workforce reduction earlier this year, impacting approximately 8,000 workers.
The plaintiffs claim that instead of relying on managerial evaluations, Meta’s internal AI systems were utilized to score and rank employees. This approach, they argue, did not adequately consider approved leave periods, thus unfairly penalizing those who had taken family or medical leave or who required disability accommodations. Among the complainants is a scientist who was notified of her job termination just days before she was due to give birth. Another involved party is an engineer who saw his performance rating suffer due to an injury-related absence, while a manager was reportedly laid off shortly after commencing medical leave.
In response to these grievances, the employees are seeking a court injunction to pause the layoffs while the case unfolds. Their demands also include reinstatement, back pay, and benefits, in addition to an independent review of the AI systems employed by Meta. Meanwhile, the company has refuted these claims, maintaining that all decisions regarding workforce and organizational changes are executed by people, not artificial intelligence systems.
This lawsuit emerges amid intensifying scrutiny over the application of AI in workplace decisions. Critics have voiced concerns that such automated systems might inadvertently introduce biases, particularly against employees exercising their rights to legally protected leave. As the case develops, it highlights the ongoing debate surrounding the ethical use of AI in corporate environments and the potential for unintended discrimination.