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Illustration showing brain opening, revealing data workersRose Wong hide On a chilly New York afternoon in February, Leon Campbell hunkered down at his desk in an office in midtown Manhattan. He cued up a podcast about gaming and launched a software platform on his laptop. For the next few hours he clicked on the corners of vehicles in images, prompting the software to draw boxes around them.
By identifying pictures of cars and SUVs, Campbell and others were creating reams of data for training algorithms such as the ones in autonomous vehicles.
These companies claim to provide “fair-trade” data work. Do they?
It was monotonous work, said Campbell, who is 24 and autistic. A lot of human labor goes into building artificial-intelligence systems. Much of it is in cleaning, categorizing, and labeling data before AIs ingest it to look for patterns.
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They log in to online platforms for anywhere from a few minutes to several hours a day, perhaps distinguishing between bunches of green onions and stalks of celery or between cat-eye and aviator-style sunglasses.
As detailed in the recent book Ghost Work by Mary Gray and Siddharth Suri, most of them are gig workers with low pay, insecure employment, and no opportunity for career advancement.
A small group of data annotation firms aims to rewrite that narrative. There they perform tasks contracted out by AI firms that pay the platforms slivers of a cent per minute. In the cutthroat competition for business, these platforms compete on scale, speed, and cost. Appen, for example, boasts a pool of one million contractors who perform tasks such as categorizing medical images or translating text for chatbots.
However, there are no regulations and only weak industry standards for what ethical sourcing means. The firm has 2, full-time employees, most based in India; half are women, who can get up to six months of maternity leave.
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People in Kenya and Uganda working for data services provider Samasource also have full-time jobs with benefits such as health care, pensions, subsidized meals, and 90 days of maternity leave. Alegion and CloudFactory, by contrast, both offer mainly hourly contract work—the norm among data annotation firms—rather than full-time employment.
The companies also vary in how much they publicly report.
CloudFactory, on the other hand, published a social impact report in but has not put one out since. Getting into the US market In their bid to expand, companies like Alegion and iMerit are also trying to build a pool of data workers in the US, drawing on underprivileged and marginalized populations there. That gives them lucrative access to government, financial, and health care clients that demand stringent security measures, work with regulated medical and financial data, or need the work done in the US for other legal reasons.
To recruit those US workers, the impact firms can go through companies like Daivergent, which serves as a conduit to organizations such as the Autism Society and Autism Speaks.
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Alegion also did a trial using tech world online earnings reviews provided through IAM23, a support group for military veterans. But trying to bring a model based on overseas outsourcing to the US presents a problem.
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When the company set up shop in New Orleans last year, promising new jobs, Mayor Mitch Landrieu publicly congratulated it. But after more than a year, the company has just 30 full-time employees in the city. But the race-to-the-bottom pricing in the data labeling industry helps reinforce a general perception that the work is simple and should be cheap.
Competing with mainstream for-profit firms has also been tough for Samasource, which Leila Janah launched as a nonprofit 11 years ago.
So she switched to a hybrid model in which the founding nonprofit owns the majority of the shares in a for-profit company that can raise capital from investors. Alegion But it came with a compromise. Samasource decided that to woo investors, it had no choice but to give them voting rights.
In the end it may be only regulation tech world online earnings reviews changes labor practices.
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