The public’s understanding of artificial intelligence (AI) is largely shaped by pop culture — by blockbuster movies like “The Terminator” and their doomsday scenarios of machines going rogue and destroying humanity. This kind of AI narrative is also what grabs the attention of news outlets: a Google engineer claiming that its chatbot was sentient was among the most discussed AI-related news in recent months, even reaching Stephen Colbert’s millions of viewers. But the idea of superintelligent machines with their own agency and decision-making power is not only far from reality — it distracts us from the real risks to human lives surrounding the development and deployment of AI systems. While the public is distracted by the specter of nonexistent sentient machines, an army of precarized workers stands behind the supposed accomplishments of artificial intelligence systems today.

Many of these systems are developed by multinational corporations located in Silicon Valley, which have been consolidating power at a scale that, journalist Gideon Lewis-Kraus notes, is likely unprecedented in human history. They are striving to create autonomous systems that can one day perform all of the tasks that people can do and more, without the required salaries, benefits or other costs associated with employing humans. While this corporate executives’ utopia is far from reality, the march to attempt its realization has created a global underclass, performing what anthropologist Mary L. Gray and computational social scientist Siddharth Suri call ghost work: the downplayed human labor driving “AI”.

Tech companies that have branded themselves “AI first” depend on heavily surveilled gig workers like data labelers, delivery drivers and content moderators. Startups are even hiring people to impersonate AI systems like chatbots, due to the pressure by venture capitalists to incorporate so-called AI into their products. In fact, London-based venture capital firm MMC Ventures surveyed 2,830 AI startups in the EU and found that 40% of them didn’t use AI in a meaningful way.

Far from the sophisticated, sentient machines portrayed in media and pop culture, so-called AI systems are fueled by millions of underpaid workers around the world, performing repetitive tasks under precarious labor conditions. And unlike the “AI researchers” paid six-figure salaries in Silicon Valley corporations, these exploited workers are often recruited out of impoverished populations and paid as little as $1.46/hour after tax. Yet despite this, labor exploitation is not central to the discourse surrounding the ethical development and deployment of AI systems. In this article, we give examples of the labor exploitation driving so-called AI systems and argue that supporting transnational worker organizing efforts should be a priority in discussions pertaining to AI ethics.

We write this as people intimately connected to AI-related work. Adrienne is a former Amazon delivery driver and organizer who has experienced the harms of surveillance and unrealistic quotas established by automated systems. Milagros is a researcher who has worked closely with data workers, especially data annotators in Syria, Bulgaria and Argentina. And Timnit is a researcher who has faced retaliation for uncovering and communicating the harms of AI systems.

Treating Workers Like Machines

Much of what is currently described as AI is a system based on statistical machine learning, and more specifically, deep learning via artificial neural networks, a methodology that requires enormous amounts of data to “learn” from. But around 15 years ago, before the proliferation of gig work, deep learning systems were considered merely an academic curiosity, confined to a few interested researchers.

In 2009, however, Jia Deng and his collaborators released the ImageNet dataset, the largest labeled image dataset at the time, consisting of images scraped from the internet and labeled through Amazon’s newly introduced Mechanical Turk platform. Amazon Mechanical Turk, with the motto “artificial artificial intelligence,” popularized the phenomenon of “crowd work”: large volumes of time-consuming work broken down into smaller tasks that can quickly be completed by millions of people around the world. With the introduction of Mechanical Turk, intractable tasks were suddenly made feasible; for example, hand-labeling one million images could be automatically executed by a thousand anonymous people working in parallel, each labeling only a thousand images. What’s more, it was at a price even a university could afford: crowdworkers were paid per task completed, which could amount to merely a few cents.

The ImageNet dataset was followed by the ImageNet Large Scale Visual Recognition Challenge, where researchers used the dataset to train and test models performing a variety of tasks like image recognition: annotating an image with the type of object in the image, such as a tree or a cat. While non-deep-learning-based models performed these tasks with the highest accuracy at the time, in 2012, a deep-learning-based architecture informally dubbed AlexNet scored higher than all other models by a wide margin. This catapulted deep-learning-based models into the mainstream, and brought us to today, where models requiring lots of data, labeled by low-wage gig workers around the world, are proliferated by multinational corporations. In addition to labeling data scraped from the internet, some jobs have gig workers supply the data itself, requiring them to upload selfies, pictures of friends and family or images of the objects around them.

Unlike in 2009, when the main crowdworking platform was Amazon’s Mechanical Turk, there is currently an explosion of data labeling companies. These companies are raising tens to hundreds of millions in venture capital funding while the data labelers have been estimated to make an average of $1.77 per task. Data labeling interfaces have evolved to treat crowdworkers like machines, often prescribing them highly repetitive tasks, surveilling their movements and punishing deviation through automated tools. Today, far from an academic challenge, large corporations claiming to be “AI first” are fueled by this army of underpaid gig workers, such as data laborers, content moderators, warehouse workers and delivery drivers.

Content moderators, for example, are responsible for finding and flagging content deemed inappropriate for a given platform. Not only are they essential workers, without whom social media platforms would be completely unusable, their work flagging different types of content is also used to train automated systems aiming to flag texts and imagery containing hate speech, fake news, violence or other types of content that violates platforms’ policies. In spite of the crucial role that content moderators play in both keeping online communities safe and training AI systems, they are often paid miserable wages while working for tech giants and forced to perform traumatic tasks while being closely surveilled.

Every murder, suicide, sexual assault or child abuse video that does not make it onto a platform has been viewed and flagged by a content moderator or an automated system trained by data most likely supplied by a content moderator. Employees performing these tasks suffer from anxiety, depression and post-traumatic stress disorder due to constant exposure to this horrific content.

Besides experiencing a traumatic work environment with nonexistent or insufficient mental health support, these workers are monitored and punished if they deviate from their prescribed repetitive tasks. For instance, Sama content moderators contracted by Meta in Kenya are monitored through surveillance software to ensure that they make decisions about violence in videos wit