On January 18, Time magazine published revelations that alarmed if not necessarily surprised many who work in Artificial Intelligence. The news concerned ChatGPT, an advanced AI chatbot that is both hailed as one of the most intelligent AI systems built to date and feared as a new frontier in potential plagiarism and the erosion of craft in writing. Many had wondered how ChatGPT, which stands for Chat Generative Pre-trained Transformer, had improved upon earlier versions of this technology that would quickly descend into hate speech. The answer came in the Time magazine piece: dozens of Kenyan workers were paid less than $2 per hour to process an endless amount of violent and hateful content in order to make a system primarily marketed to Western users safer. It should be clear to anyone paying attention that our current paradigm of digitalisation has a labour problem. We have and are pivoting away from the ideal of an open internet built around communities of shared interests to one that is dominated by the commercial prerogatives of a handful of companies located in specific geographies.

A version of this article originally appeared in Issues in Science and Technology. When Americans talk about automation, they tend to ask first how many jobs are at risk--or more broadly, how many jobs will there be, who will do them, and where will they be located. These are the wrong questions. They suggest a policy discussion that starts at the end, focused on mitigating negative impacts. This approach perpetuates a flawed view of how technology develops--one that plagues contemporary debates about the future of work--because it presents technological progress as a process of scientists and engineers applying knowledge and technique to the material world to find a single best way to perform some task.In short, this view of automation sees the consequences of technology for workers (job loss, lower wages, need for retraining, and the like) as largely inevitable.

Maureen Donnelly took a job with Amazon when the retail goliath opened a fulfillment center on Staten Island in September 2018. The 46-year-old Staten Islander quit after just one month. Last week, more than 100 workers and their supporters gathered outside the same 855,000-square-foot packing plant to protest working conditions and spotlight newly released data showing the rate of worker injury there was three times higher than the national average for similar warehouse work. Here Donnelly tells Post reporter Dean Balsamini what it was like to work for Jeff Bezos' Amazon, which the National Council for Occupational Safety and Health included on its 2019 "Dirty Dozen" list of the nation's most dangerous employers. Amazon did not immediately return comment. I've been a waitress, a newsroom clerk, an EMT and spent summers on a dairy farm in Ireland.

Take a look at the tag on your shirt. If you are in the U.S., chances are it was made in a country like China or Thailand and then shipped overseas. Jonathan Zornow, the sole employee of a new startup called Sewbo, thinks the U.S. could bring garment manufacturing a little closer to home by automating the feeding of fabric into sewing machines--a step that to this day is done by hand. Zornow has created a process by which a robotic arm guides chemically stiffened pieces of fabric through a commercial sewing machine. Machines already play a large part in clothing manufacturing.

Machine learning has a critical dependency on human learning. I'm not referring to data scientists, a class of learned humans who play an undeniably pivotal role in this new era. What I'm referring to are the legions of individuals who prepare training data to guide algorithms in their search for patterns of interest. Once the target patterns have been tagged and flagged by humans in the know, machine learning and other artificial intelligence (AI) algorithms can work their magic. Does it make sense to demean this job category that's essential to the cognitive era?