According to Braden-Harder, the bulk of Appen’s business involves two main customers.
“We’re basically talking about Facebook and Google here,” she says. And now those companies are taking a hit as the global economy slows the growth of digital advertising, and Apple’s new privacy features are also affecting Facebook’s ad revenue.
Simply put, Appen’s top customers sneezed and Appen caught a cold.
Analysts covering the company reacted harshly, and neither did JP Morgan’s Bob Chen, which slashed its valuation of Appen late last month to just $3. Appen has not traded at these levels since 2017.
“His biggest customers are starting to feel the impacts of a weaker macro [economic conditions] and have begun to cut capital expenditures, which has resulted in a significant decline in Appen’s revenue base and we have limited visibility on when this might improve,” Chen said.
Meanwhile, Macquarie analysts cited another potential downside risk related to competitive pressure on pricing as well as the risk that big tech will reduce its reliance on contractors like Appen.
And Appen’s woes mask another crucial issue for its future: the crowdsourcing ethics it engages in.
The question was raised earlier this year when the company featured prominently in an MIT Technology Review series. The series explored the idea that the AI industry is creating a new colonial world order with crowdsourcing platforms in a race to the bottom to find – and exploit – low-paid workers around the world. It was titled: How the AI Industry Profits from a Disaster.
He focused on data-tagging platforms like Appen and the millions they outsource for this work – the so-called “ghost workers”. These workers tag data for tech giants via small chunks of work that earn equally modest payouts. The viability of Appen and competing platforms hinges on their ability to assign and pay for this work with as little human intervention as possible.
This pits Appen against workers for a share of every dollar earned. For fiscal 2021, Appen generated revenue totaling $447.3 million ($671.2 million). It paid $268.4 million for crowd-tagging services, but the average salary of its more than one million workers that year was around $268 ($391).
Appen is also up against other data tagging platforms that scour the world for the cheapest labor. If it’s a generic job that can be done anywhere, then “you can definitely do a run-down,” said former Appen boss Braden-Harder.
This is one of the reasons she left Appen soon after the IPO with the growing pressure to maximize returns for investors.
“I kind of knew it was going to go wrong. There had already been pressure,” she said.
“I knew with this business model there weren’t too many options for a CEO, in terms of giving Australian investors what they seemed to be looking for.”
The MIT series examined how these platforms arrived in Venezuela following the collapse of its economy, which plunged its middle class into poverty and drove the demand for any source of employment. Venezuela’s economic collapse has produced the magic combination of a desperate but educated workforce and internet connectivity.
Oskarina Fuentes Anaya was one of many people forced to turn to Appen as their only source of work. She fled Venezuela for Colombia. His situation was exacerbated by a chronic illness that limited his work options, but Fuentes soon learned what it was like to have his life ruled by the platform’s algorithms that ensured the most economical distribution of labor to people. more than one million Appen employees.
“We all help each other,” Fuentes told MIT of the support these workers gave each other to share what little work was available.
The MIT story chronicles pay cuts, desperation to grab dwindling available work, and account suspensions – which also triggered pay suspensions with limited reliance on a human operator from the platforms.
“What started in Venezuela created an expectation among AI industry players as to how little they should have to pay for such services, and it created a playbook of how to respect the prices customers rely on,” the MIT story explains. .
While data labeling provided a lifeline for workers like Anaya, it also exposed them to a Darwinian scale of exploitation as platforms slashed their pay and suspended accounts – and livelihoods – in a continuous downward race.
The perils include harsh customer criticism that can result in account suspension, ambiguous tasks, and administrative errors that can cause an account to be suspended for months.
Julian Posada, an associate professor at Yale who has studied these crowdsourcing services in South America, says there is a huge power imbalance that favors platforms that have the power to set their own rules. They can literally travel the world in search of cheap labor to perform these menial tasks.
But Venezuela’s educated population, the great infrastructure from before its oil economy collapsed — provided a rare combination of ingredients that made it perfect for these contractors, Posada says.
“So on the one hand you have the infrastructure for the work. On the other hand you have people who are in crisis with the highest levels of inflation, so you can pay them as low as possible “says Posada.
At first it was good work.
To build a viable network of contributors, these platforms offered bonuses, and in one case even paid these outsourced workers an hourly rate. But once they reached critical mass, many of those payments disappeared and pay rates fell.
In one case, a platform investigated by Posada accidentally left its payment data for thousands of workers on a public Google spreadsheet.
He says it provided a clear picture of the relationship between rising attendance and falling wages.
“The more people joined, the less people earned,” he says.
As the situation slowly improves in Venezuela, with rising oil prices, the trick will be to find the next low-cost job market with enough people desperate to find work.
“The next time there’s a country in crisis, they’ll probably be there, as long as there are computers and desperate people,” Posada says.
After the MIT story, Appen began emphasizing its treatment of its outsourced workforce which includes the company’s code of ethics.
He cited an internal survey of 7,000 workers late last year indicating that 17% were long-term unemployed before joining Appen, 16% lived below the global poverty line. Sixty-three percent used Appen income to support their household or pay for their education.
But another figure was telling. In its annual report, Appen reported the survey showing that 67% identified Appen as their main source of income.
In response to questions, Appen said, “We are committed to providing fair pay and ethical treatment to our crowd. Our Crowd Code of Ethics explicitly states that our goal is to pay our crowd above minimum wage in all markets worldwide in which we operate. To help guide our clients, we have a fair compensation feature available on our platform. »
Appen also adjusts its pay per task to the minimum wage of the worker’s locality. This means that workers from a poor country are paid less for performing the same task as someone from a richer country. In the MIT story, Appen said he saw an increase in fraud where users used VPNs to access higher compensation offers in other countries.
Braden-Harder, for his part, is unimpressed with the rhetoric about the minimum wage that is set by US states and tends to be very low.
“You can pay the legal minimum wage and still pay poverty wages,” she says.
Posada cited a recent fair labor project that looked at working conditions on all crowdsourcing platforms and found that none met minimum standards. But Appen was the best of a bad bunch.
“It’s like, the best of the worst. They have standards, they have rules in place,” he says.
Braden-Harder has retired from his leadership role and is currently a member of the advisory board of the Global Social Benefit Institute at Santa Clara University.
She helps mentor global start-ups like the one run in Kenya by an Australian university graduate who provides school meals.
“I think all of us, myself included, believe that businesses can do things for good, but you have to have the right business model,” she says.
When it comes to solving the problem of crowdsourcing, Braden-Harder says big companies need to change the way they think about buying these services.
“In my experience, sourcing is the bad side of any business, because the same guy who buys toilet paper for big companies also buys those services.”
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