Massive Hidden Labour
Amazon’s Super Bowl commercial #BeforeAlexa was streamed to millions of viewers. In this 90-second ad, Alexa was portrayed as several obsolete roles in different historical scenarios — a maid that lowered room temperature by breaking a window of her master’s house; a joker that told a joke by deprecating himself to amuse the queen; a secretary received the command to “delete the tapes” for a President Nixon character. The commercial paints a picture that Alexa is now all in one our new maid, new joker and new secretary (the implied power relation is another topic). From the eyes of consumers, Alexa seems to save a good amount of labour for us today. However, if you have read Anatomy of an AI System, you will know that behind every execution of a simple command you give to Alexa, there is a massive chain of human labour that are out of our sights — the mineral extraction for making lithium-ion batteries, the manufacturing of electronic parts, the tagging and labeling of data sets, and more.
Lithium and Cobalt Mining
Lithium is a key material for making lithium-ion batteries, a type of rechargeable battery commonly used for smart devices and electric cars. The rise of smart electronics has led to the rise of lithium mining projects and complex supply chains. Lithium mining has sparked controversy for its environmental cost and the damage to the indigenous communities in some of the mining sites such as Bolivia. Moreover, according to Washington Post, a consumer company typically deals with multiple suppliers who also deal with multiple suppliers. The complexity of the supply chain makes the sourcing of lithium difficult to track. Cobalt is another key element for powering lithium-ion batteries. The demand for cobalt also led a massive number of mining projects in Democratic Republic of the Congo (DRC). Human right issues in cobalt mining operations are not rare. Amnesty reported that in some sites, the workers, including children, are working in harsh and dangerous conditions without protection for $1 USD per day.
Chinese Electronic Factory Workers
To lower the cost for electronic manufacturing, multinational companies find their supplies from China for cheaper labor cost. In 2011, China Labour Watch (CLW) revealed the horrible working conditions in ten Chinese electronics factories who assembles products for well known companies such as Dell, Salcomp, IBM, Ericsson, Philips, Microsoft, Apple, HP, and Nokia. According to the report, some workers were required to work overtime up to 160 hours per month. Majority of these factories did not offer wage that covers basic living costs. The intensity of working pace also lead to long-term occupational illness and injury.
Crowdsourced Online Workers
AI learn from data, and data need to be organized into training data sets. To compile training data sets, manually labeling, tagging and flagging thousands of data features are common. To save the cost of hiring someone to do this type of repetitive tasks, companies outsourced the tasks to people who are willing to take on these tasks for compensations that are far below minimum wages. Amazon’s Mechanical Turk is the largest crowdsourcing platform to the date. According to this paper, the median hourly wage earned by Turk workers is only about $2 USD per hour; only 4% of them earned more than $7.25 USD per hour.
And Us
Again, AI learn from data, including ours. As we are interacting with Alexa or other AI systems, our interactions are turned into data and transmitted to the companies behind the systems. In other words, as we are sending out commands to the AI and provide feedback about the accuracy of their responses, we are also providing labor.
AI Are Essential. Or Are They?
The commercial humorously exaggerated the inconveniences without an AI Virtual Assistant, and they are simply untrue. Without Alexa, we walk a few steps to turn up the knob of the thermometer; we ask a real human right beside to tell us a joke, and we set a reminder ourselves without the need of having a secretary. Behind each simple command, there is a massive labour that sustain an AI system that’s hidden from us. As a consumer, we should know that the stack required to sustain an AI system is not just data modeling, servers or networks; it goes further into capital, labour and nature. And as Kate Crowd pointed out, we should all start doing thought experiments to compare the the cost of the planetary labour network Alexa requires to play our favourite song versus the cost of us hitting the play ourselves.
