Bringing Up AI: How People Are Teaching Their Jobs to Machines – NewCo Shift

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The economy stands at a threshold moment in the era of machine learning. The artificial intelligences that companies are increasingly deploying are just beginning to take on roles and jobs that used to demand a human being at the controls. But in most cases theyre nowhere near ready to take over entirely. They still need people at their sidesin some cases to generate the data that will train them, in others to provide judgment thats beyond them.

Welcome to the world of the hybrid human-machine workplace. A couple of recent articles have begun to give us a portrait of this emerging work environment, with its awkward encounters, unemployment fears, and potential for both efficiency and exploitation.

In Wired, Davey Alba talked with a bunch of people who screen YouTube videos for content that might offend advertisers. In the long run, Google (which owns YouTube) aims to hand this task over to an AI. But the judgments involved are complex, opaque, and subjective, and distressed advertisers arent going to wait for the technology to mature. So low-paid, part-time contractors hired through an agency called ZeroChaos do the work. Their video ratings serve two purposesprotecting YouTubes revenue right now, and building up a trove of data to help the AI learn what humans (and advertisers) find objectionable.

A jobs a job, and a lot of the people doing this one are glad to have it. But its high-pressure, high-volume piecework, and working for Google sometimes feels like working for an inhuman AI; the company barely communicates with workers, dismisses them precipitously and without explanation, and provides no benefits, job security, or guarantee of steady work.

The problem with treating your AI tutors this way isnt just a matter of ethicsit could also warp the outcome of the whole project. As Alba puts it: if it turns out youre training your AI mainly on the perceptions of anxious temp workers, they could wind up embedding their own distinct biases in those systems.

Machine-learning tools are extending their reach far beyond the giant tech platforms that pioneered them. In The New York Times, Daisuke Wakabayashi offers a compendium of case studies of the propagation of AI techniques into other industries.

At Lola, a travel-booking app, human travel agents have been guiding the education of an AI named Harrison that has become proficient at recommending hotels. (The human agents are still better at offering users travel tips, or helping with upgrades.)

Legal Robot is developing another AI that can parse complex contracts and other legal documents, identifying problematic passages and suggesting improvements. Its CEO points out that legal agreementswith their repetition, formality, and structured naturemake good fodder for machine learning.

At Magoosh, the test-prep company, customer service reps are speeding up their answers to incoming student questions now that they have an AI at their disposal thats gotten steadily better at suggesting email replies. But employees dont think theyre going to get edged out any time soon: Too many questions still require human intuition, and people are still better than AIs at knowing when it makes sense to break a rule.

In all these cases, the relationship between human worker and AI is neither coexistence nor warfare but rather a continuous process of reaction, adjustment, and evolutionary change. The technologys advances have been prodigious, yet it still cant do most of the things we expect it to eventually master. Were still waiting to find out just where people will fit in when these software machines have caught up with our imaginations.

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Bringing Up AI: How People Are Teaching Their Jobs to Machines - NewCo Shift