In the fall of 2020, gig workers in Venezuela posted a series of images to online forums where they gathered to talk shop. The photos were mundane, if sometimes intimate, household scenes captured from low angles—including some you really wouldn’t want shared on the Internet. 

In one particularly revealing shot, a young woman in a lavender T-shirt sits on the toilet, her shorts pulled down to mid-thigh.

The images were not taken by a person, but by development versions of iRobot’s Roomba J7 series robot vacuum. They were then sent to Scale AI, a startup that contracts workers around the world to label audio, photo, and video data used to train artificial intelligence. 

They were the sorts of scenes that internet-connected devices regularly capture and send back to the cloud—though usually with stricter storage and access controls. Yet earlier this year, MIT Technology Review obtained 15 screenshots of these private photos, which had been posted to closed social media groups. 

The photos vary in type and in sensitivity. The most intimate image we saw was the series of video stills featuring the young woman on the toilet, her face blocked in the lead image but unobscured in the grainy scroll of shots below. In another image, a boy who appears to be eight or nine years old, and whose face is clearly visible, is sprawled on his stomach across a hallway floor. A triangular flop of hair spills across his forehead as he stares, with apparent amusement, at the object recording him from just below eye level.

The other shots show rooms from homes around the world, some occupied by humans, one by a dog. Furniture, décor, and objects located high on the walls and ceilings are outlined by rectangular boxes and accompanied by labels like “tv,” “plant_or_flower,” and “ceiling light.” 

iRobot—the world’s largest vendor of robotic vacuums, which Amazon recently acquired for $1.7 billion in a pending deal—confirmed that these images were captured by its Roombas in 2020. All of them came from “special development robots with hardware and software modifications that are not and never were present on iRobot consumer products for purchase,” the company said in a statement. They were given to “paid collectors and employees” who signed written agreements acknowledging that they were sending data streams, including video, back to the company for training purposes. According to iRobot, the devices were labeled with a bright green sticker that read “video recording in progress,” and it was up to those paid data collectors to “remove anything they deem sensitive from any space the robot operates in, including children.”

In other words, by iRobot’s estimation, anyone whose photos or video appeared in the streams had agreed to let their Roombas monitor them. iRobot declined to let MIT Technology Review view the consent agreements and did not make any of its paid collectors or employees available to discuss their understanding of the terms.

While the images shared with us did not come from iRobot customers, consumers regularly consent to having our data monitored to varying degrees on devices ranging from iPhones to washing machines. It’s a practice that has only grown more common over the past decade, as data-hungry artificial intelligence has been increasingly integrated into a whole new array of products and services. Much of this technology is based on machine learning, a technique that uses large troves of data—including our voices, faces, homes, and other personal information—to train algorithms to recognize patterns. The most useful data sets are the most realistic, making data sourced from real environments, like homes, especially valuable. Often, we opt in simply by using the product, as noted in privacy policies with vague language that gives companies broad discretion in how they disseminate and analyze consumer information. 

Did you participate in iRobot’s data collection efforts? We’d love to hear from you. Please reach out at tips@technologyreview.com. 

The data collected by robot vacuums can be particularly invasive. They have “powerful hardware, powerful sensors,” says Dennis Giese, a PhD candidate at Northeastern University who studies the security vulnerabilities of Internet of Things devices, including robot vacuums. “And they can drive around in your home—and you have no way to control that.” This is especially true, he adds, of devices with advanced cameras and artificial intelligence—like iRobot’s Roomba J7 series.

This data is then used to build smarter robots whose purpose may one day go far beyond vacuuming. But to make these data sets useful for machine learning, individual humans must first view, categorize, label, and otherwise add context to each bit of data. This process is called data annotation.

“There’s always a group of humans sitting somewhere—usually in a windowless room, just doing a bunch of point-and-click: ‘Yes, that is an object or not an object,’” explains Matt Beane, an assistant professor in the technology management program at  the University of California, Santa Barbara, who studies the human work behind robotics.

The 15 images shared with MIT Technology Review are just a tiny slice of a sweeping data ecosystem. iRobot has said that it has shared over 2 million images with Scale AI and an unknown quantity more with other data annotation platforms; the company has confirmed that Scale is just one of the data annotators it has used. 

James Baussmann, iRobot’s spokesperson, said in an email the company had “taken every precaution to ensure that personal data is processed securely and in accordance with applicable law,” and that the images shared with MIT Technology Review were “shared in violation of a written non-disclosure agreement between iRobot and an image annotation service provider.” In an emailed statement a few weeks after we shared the images with the company, iRobot CEO Colin Angle said that “iRobot is terminating its relationship with the service provider who leaked the images, is actively investigating the matter, and [is] taking measures to help prevent a similar leak by any service provider in the future.” The company did not respond to additional questions about what those measures were. 

Ultimately, though, this set of images represents something bigger than any one individual company’s actions. They speak to the widespread, and growing, practice of sharing potentially sensitive data to train algorithms, as well as the surprising, globe-spanning journey that a single image can take—in this case, from homes in North America, Europe, and Asia to the servers of Massachusetts-based iRobot, from there to San Francisco–based Scale AI, and finally to Scale’s contracted data workers around the world (including, in this instance, Venezuelan gig workers who posted the images to private groups on Facebook, Discord, and elsewhere). 

Together, the images reveal a whole data supply chain—and new points where personal information could leak out—that few consumers are even aware of. 

“It’s not expected that human beings are going to be reviewing the raw footage,” emphasizes Justin Brookman, director of tech policy at Consumer Reports and former policy director of the Federal Trade Commission’s Office of Technology Research and Investigation. iRobot would not say whether data collectors were aware that humans, in particular, would be viewing these images, though the company said the consent form made clear that “service providers” would be.

“It’s not expected that human beings are going to be reviewing the raw footage.”

“We literally treat machines differently than we treat humans,” adds Jessica Vitak, an information scientist and professor at the University of Maryland’s communication department and its College of Information Studies. “It’s much easier for me to accept a cute little vacuum, you know, moving around my space [than] somebody walking around my house with a camera.” 

And yet, that’s essentially what is happening. It’s not just a robot vacuum watching you on the toilet—a person may be looking too. 

The robot vacuum revolution 

Robot vacuums weren’t always so smart. 

The earliest model, the Swiss-made Electrolux Trilobite, came to market in 2001. It used ultrasonic sensors to locate walls and plot cleaning patterns; additional bump sensors on its sides and cliff sensors at the bottom helped it avoid running into objects or falling off stairs. But these sensors were glitchy, leading the robot to miss certain areas or repeat others. The result was unfinished and unsatisfactory cleaning jobs. 

The next year, iRobot released the first-generation Roomba, which relied on similar basic bump sensors and turn sensors. Much cheaper than its competitor, it became the first commercially successful robot vacuum.

The most basic models today still operate similarly, while midrange cleaners incorporate better sensors and other navigational techniques like simultaneous localization and mapping to find their place in a room and chart out better cleaning paths. 

Higher-end devices have moved on to computer vision, a subset of artificial intelligence that approximates human sight by training algorithms to extract information from images and videos, and/or lidar, a laser-based sensing technique used by NASA and widely considered the most accurate—but most expensive—navigational technology on the market today. 

Computer vision depends on high-definition cameras, and by our count, around a dozen companies have incorporated front-facing cameras into their robot vacuums for navigation and object recognition—as well as, increasingly, home monitoring. This includes the top three ro