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Lawsuit Accuses Meta of Using AI to Lay Off Employees on Leave

Meta is facing a legal challenge from numerous employees who accuse the company of using artificial intelligence tools to determine layoffs, allegedly targeting those on maternity leave, medical leave, and employees with disabilities. The lawsuit, lodged in a California federal court, argues that Meta employed AI-powered performance evaluations and activity data, including productivity metrics, to select workers for job cuts during a workforce reduction that impacted about 8,000 employees earlier this year.

The complaint contends that Meta’s reliance on internal AI systems to score and rank employees bypassed the traditional manager assessments. The plaintiffs assert that these systems did not consider approved leave periods, which led to lower performance scores for those on family or medical leave or those who had disability accommodations. Among the individuals involved in the lawsuit is a scientist who received notice of her termination just two days before she was set to give birth. Another case involves an engineer whose performance rating allegedly suffered due to his absence while recovering from an injury, and a manager who claims he was laid off shortly after starting medical leave.

The employees involved in the lawsuit are asking the court to halt the layoffs as the case unfolds. They are also seeking reinstatement, compensation for lost wages and benefits, and demand an independent review of Meta’s AI systems. This lawsuit highlights concerns over the increasing use of AI in workplace decisions, with critics warning that such automated systems might inadvertently introduce bias against those taking legally protected leave.

Meta has refuted the allegations, maintaining that decisions regarding workforce and organizational changes are made by human managers rather than relying solely on artificial intelligence systems. The case underscores a broader debate over the role of AI in employment practices, as more companies integrate technology into their decision-making processes.

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