According to Braden-Harder, the bulk of Appen’s business works with two primary customers.
“We’re mainly talking about Facebook and Google here,” she says. And now these companies are taking a big hit as the global economy slows the growth of digital ads, and Apple’s new privacy features have also hit Facebook’s ad revenue.
In simple terms, top Appen customers sneezed and Appen caught a cold.
Analysts covering the company’s business have responded brutally, no more than Bob Chen of JP Morgan, who lowered his Appen valuation late last month to just $3. Appen hasn’t traded at those levels since 2017.
Its largest clients are beginning to feel the effects of the weaker macroeconomics [economic conditions] And we’ve started cutting investment spending, and that’s significantly reduced Appen’s core revenue and we have limited visibility on when that could improve, Chen says.
Meanwhile, Macquarie analysts have pointed to more potential downside risks from competitive pricing pressures as well as the risks of big technology reducing its dependence on outside contractors like Appen.
Abyan’s problems also obscure another important issue regarding its future: the ethics of crowdsourcing in which it is involved.
The issue was brought up early this year when the company featured prominently in the MIT Technology Review series. The series explored the idea that the AI sector is creating a neo-colonial global order with crowdsourcing platforms in a race to the bottom to find and exploit low-wage workers around the world. It was titled: How the Artificial Intelligence Industry is Benefiting from the Disaster.
I focused on data classification platforms like Appen, and the millions they pool for this work – the so-called “dummy workers”. These workers label data for tech giants with small parts of the work that earn equally modest payments. The viability of Appen and competing platforms depends on their ability to hire and pay for this work with as little human intervention as possible.
It pits Appen against workers for a share of every dollar earned. For fiscal year 2021, Appen generated revenue totaling $447.3 million ($671.2 million). It paid $268.4 million for public tagging services, but the average wage for its 1 million-plus workers that year was about $268 ($391).
Appen is also in conflict with other data classification platforms that scour the world for the cheapest workforce. If public action can be done anywhere, then “you can certainly do a race to the bottom,” says former Appen chief Braden-Harder.
This was one of the reasons it left Appen shortly after its IPO as pressure mounted to maximize investor returns.
“I kind of knew it was going to get bad. There was stress already,” she says.
“I knew that with this business model, there wasn’t much choice for any CEO, in terms of giving Australian investors what they were apparently looking for.”
The MIT series looked at how these platforms landed on Venezuela after its economy collapsed, driving the middle class into poverty and driving demand for any source of employment. Venezuela’s economic meltdown has produced a magical mixture of a desperate, educated workforce and an internet connection.
Oskarina Fuentes Anaya was one of many forced to turn to Appen as their sole source of work. I fled from Venezuela to Colombia. Her condition was exacerbated by a chronic illness that limited her work options, but Fuentes soon learned how her life was governed by platform algorithms that ensured economic work was sent to more than a million Appen workforce.
We all help each other out,” Fuentes told MIT about the support these workers gave each other to share what little work was available.
The MIT story chronicles wage cuts, desperation to seize dwindling available work, and account suspensions—which also led to wage suspensions with limited recourse to the human factor of platforms.
“What started in Venezuela set an expectation among players in the AI industry about how much shortages they would have to pay for such services, and also created a guide to how to meet the prices that customers depend on,” the MIT story says. .
While data disaggregation has provided a lifeline for workers like Anaya, it has also exposed them to a Darwinian scale of exploitation as platforms cut their salaries, suspended accounts — and livelihoods — in a constant race to the bottom.
Risks include harsh customer reviews that can lead to account suspension, ambiguous tasks and administrative errors that can lead to account suspension for several months.
Julian Posada, an assistant professor at Yale who has studied crowdsourcing services in South America, says there is a huge power imbalance that favors platforms that have the ability to make their own rules. They can literally search the world for cheap labor to perform these menial tasks.
Posada says Venezuela’s educated population, and the great infrastructure that existed before its oil economy collapsed – provided a rare blend of ingredients that made it ideal for these offshore contractors.
“On the other hand, you have the infrastructure to work. On the other hand, there are people who are going through a crisis with high levels of inflation, so you can pay them as little as you can,” says Posada.
At first, it was a good job.
To build a viable network of contributors, these platforms offered bonuses, and in one case even paid these outside workers an hourly wage. But once it reached critical mass, many of those payments disappeared and wage rates fell.
In one case, the Posada platform studied accidentally left its payment data for thousands of workers in a public Google spreadsheet.
He says he has provided a clear picture of the relationship between growing crowds and declining wages.
“The more people who join, the fewer the winnings,” he says.
With the situation slowly improving in Venezuela, with oil prices rising, the trick will be to find the next low-cost job market with enough desperate people to work.
“The next time a country is in crisis, they will probably be there, as long as there are computers and desperate people,” Posada says.
Following the MIT story, Appen began highlighting its treatment of its crowdsourcing workforce which includes the company’s code of ethics.
It cited an internal survey of 7,000 workers from late last year indicating that 17 percent were long-term unemployed before joining Abyan, and 16 percent were living below the global poverty line. Sixty-three percent were using Appen’s earnings to support their families or pay for education.
But another character was telling us. Appen reported in its annual report that the survey showed that 67% identified Appen as their main source of income.
In response to inquiries, Abyan said, “We are committed to fair pay and ethical treatment of our crowd. Our crowdfunding code of ethics explicitly states that our goal is to pay wages above minimum wage in all markets around the world in which we operate. To help guide our customers, we have the advantage Fair payment available on our platform.
Appen also adjusts its pay for each job to the local minimum wage for the worker. This means that workers from a poor country are paid less for doing the same task as someone from a richer country. In the MIT story, Appen said it saw a slight increase in fraud as users used VPNs to access higher salaries offered in other countries.
Braden-Harder, for example, is not a fan of talking about the minimum wage set by individual states in the United States and tends to be really low.
“You can pay the legal minimum wage while still paying the poverty wages,” she says.
Posada cited a recent Fair Business Project that looked at working conditions across all crowdsourcing platforms and found that none of them met the minimum standards. But Eben was the best among a bad group.
“It’s like, the best of the worst. They have some standards and they have some rules in place,” he says.
Braden-Harder has stepped down from executive roles and is currently an advisory board member at the Santa Clara University Global Social Benefits Institute.
She helps guide global startups like the one run in Kenya by an Australian university graduate serving school lunches.
“I think we all, including myself, believe that business can do things for good, but you have to have the right business model,” she says.
When it comes to solving the crowdsourcing problem, Braden-Harder says big companies need to change their thinking when it comes to purchasing these services.
“In my experience, buying is the evil side of any company because the same person who buys toilet paper for big companies also buys these services.”
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