Resistance is growing among Meta employees over the company’s plan to monitor their keystrokes and mouse movements as part of an effort to collect data for AI training.
Protest leaflets have appeared across the company’s offices, posted on vending machines, in bathrooms, and in meeting rooms, urging staff to join a petition drive at mcipetition.com. According to photographs obtained by Reuters, the flyers ask: “Don’t want to work at the Employee Data Extraction Factory?”
The development comes approximately one week before the parent company of Facebook and Instagram is set to lay off 10% of its workforce.
The flyers represent the most visible sign yet of an emerging labor movement within Meta, as many workers grow increasingly worried that they are actively contributing to the development of the digital tools that will eventually replace them.
Rather than offering a direct comment, a Meta spokesperson referred Reuters to a previously issued statement about the corporate computer monitoring system.
The statement explained that as the company develops AI agents to help people complete everyday tasks on computers, its models need real-world examples of how people actually use computers, including mouse movements, button clicks, and navigating pop-up menus.
The protest flyers, however, invoke U.S. labor law, stating that workers are legally protected when they choose to organize for better working conditions.
In the United Kingdom, a group of Meta employees has launched an effort to unionize with the United Tech and Allied Workers (UTAW). The workers set up a membership recruitment website using the URL “Leanin.uk,” a reference to former Meta COO Sheryl Sandberg’s bestselling book “Lean In,” which encourages women to assert an equal place in the workplace.
Elinor Payne, a UTAW organizer, said: “Meta employees are paying the price for management’s reckless and costly bets. While executives chase speculative AI strategies, staff face devastating job cuts, authoritarian surveillance, and the harsh reality of being forced to train the inefficient systems designed to replace them.”
