The revelation tells us a lot about how “smart” devices really work. Alexa-enabled speakers can and do interpret speech, but Amazon relies on human guidance to make Alexa, well, more human—to help the software understand different accents, recognize celebrity names, and respond to more complex commands. This is true of many artificial intelligence–enabled products. They’re prototypes. They can only approximate their promised functions while humans help with what Harvard researchers have called “the paradox of automation’s last mile.” Advancements in AI, the researchers write, create temporary jobs such as tagging images or annotating clips, even as the technology is meant to supplant human labor. In the case of the Echo, gig workers are paid to improve its voice-recognition software—but then, when it’s advanced enough, it will be used to replace the hostess in a hotel lobby.
A 2016 paper by researchers at Stanford University used a computer vision system to infer, with 88 percent accuracy, the political affiliation of 22 million people based on what car they drive and where they live. Traditional polling would require a full staff, a hefty budget, and months of work. The system completed the task in two weeks. But first, it had to know what a car was. The researchers paid workers through Amazon’s Mechanical Turk platform to manually tag thousands of images of cars, so the system would learn to differentiate between shapes, styles, and colors.
It may be a rude awakening for Amazon Echo owners, but AI systems require enormous amounts of categorized data, before, during, and after product launch. The ideal state is a feedback loop: The Echo performs decently, voice data from customers are collected and used to improve the service, and then more people buy it as it improves and more data are collected, further refining it. This is true for very different types of AI products.
For example, human workers watch and manually tag footage uploaded from Amazon’s Ring products, surveillance cameras that homeowners can install on their doorbells and front porches. Facebook’s content-moderation AI relies on thousands of people across the globe teaching software what counts as objectionable in different contexts. Another loop: Humans flag content, the AI gets better at detecting it, more people use Facebook as it gets safer, and the AI gets smarter as more content is flagged. In all cases, Silicon Valley would have us believe that AI is smart enough to replace humans, when in reality it only works because of the role of hidden human labor in creating and maintaining these loops. AI is always a human-machine collaboration. It can accomplish incredible feats, but rarely alone.
Astra Taylor, a writer and documentary director who studies what she calls “the automation charade,” notes that companies have a vested interest in obscuring the role of humans in automation’s last mile. “If ‘friction’ is actually knowing it’s a human being on the other side of your smart speaker,” she says, “then that’s a discomfort we have to face.”