Meta’s AI Technology Allegedly Identified Leave-Taking Employees for Layoffs

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Meta Platforms is confronting a lawsuit initiated by numerous employees who allege that the company utilized artificial intelligence systems to determine which workers to lay off, allegedly impacting those on maternity leave, medical leave, and employees with disabilities more significantly. The legal action, lodged in a federal court in California, claims that Meta’s use of AI-driven performance evaluations and analysis of employee activity data, including productivity metrics, played a crucial role in selecting individuals for job eliminations amid a workforce reduction that affected approximately 8,000 staff members earlier this year.

The lawsuit contends that instead of depending on managerial evaluations, Meta employed internal AI mechanisms to score and rank employees. The plaintiffs argue this system overlooked approved leave periods, which led to lower performance scores for employees who took family or medical leave or required disability accommodations. Among the plaintiffs is a scientist who received notice of her termination just two days before she was due to give birth. Another claimant, an engineer recovering from an injury, asserts that his performance evaluation suffered due to his absence. Additionally, a manager who had taken medical leave alleges he was laid off soon after his leave began.

The group of employees is seeking a judicial order to suspend further layoffs during the litigation process. They are also requesting reinstatement, compensation for lost wages and benefits, and an independent examination of Meta’s AI systems. Meanwhile, Meta has refuted the allegations, maintaining that human decision-makers, rather than AI systems, conduct workforce and organizational decisions.

This legal challenge emerges amid increasing apprehension about the role of AI in workplace decision-making processes. Critics caution that such automated systems may introduce bias against employees who avail themselves of legally protected leave, potentially influencing the fairness and equality of workforce management.

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