Appen’s woes threaten the low-paid workers’ livelihoods

According to Braden-Harder, most of Appen’s business is with two main clients.

“We’re basically talking about Facebook and Google here,” she says. And now these companies are taking a hit as the global economy slows the growth of digital advertising, and Apple’s new privacy features also hit Facebook’s ad revenue.

In simple terms, Appen’s top customers sneezed and Appen caught a cold.

Analysts covering the company have responded viciously, none more so than JP Morgan’s Bob Chen, who cut his valuation of Appen late last month to just $3. Appen hasn’t traded at those levels since 2017.

“Its largest clients are starting to feel the impacts of a weaker macro. [economic conditions] and we have started to reduce capex, and this has led to a significant decline in Appen’s core revenue and we have limited visibility as to when this could improve,” says Chen.

Former Appen CEO Lisa Braden-Harder.

Meanwhile, analysts at Macquarie have cited further potential downside risk from competitive pricing pressure, as well as the risk of big tech reducing its reliance on outsourcers like Appen.

And Appen’s troubles obscure another crucial issue about its future: the ethics of the crowdsourcing in which it engages.

The issue came to a head earlier this year when the company was featured prominently in an MIT Technology Review series. The series explored the idea that the AI ​​industry is creating a colonial new 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 the catastrophe.

He focused on data labeling platforms like Appen, and the millions who contribute to this work, the so-called ‘ghost workers’. These workers tag data for tech giants through small portions of work that earn equally modest pay. The viability of Appen and rival platforms depends on their ability to allocate and pay for this work with as little human intervention as possible.

He pits Appen against the 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 collective labeling services, but the median salary for its more than 1 million workers that year was about $268 ($391).

Appen is also up against other data labeling platforms that are scouring the globe for the cheapest labor. If it’s a generic job that can be done anywhere, then “you can definitely do a race to the bottom,” says former Appen boss Braden-Harder.

It was one of the reasons he left Appen shortly after the IPO with mounting pressure to maximize investor returns.

“I knew it was going to get worse. There had already been pressure,” she says.

“I knew that with this business model, there weren’t too many options for any one CEO, in terms of giving Australian investors what they seemed to be looking for.”

The MIT series looked at how these platforms descended on Venezuela following the collapse of its economy, which has pushed its middle class into poverty and fueled demand for any source of employment. Venezuela’s economic collapse produced the magical combination of a desperate but educated workforce and Internet connectivity.

Oskarina Fuentes Anaya was one of the many forced to resort to Appen as her only source of work. She fled from Venezuela to Colombia. Her situation was compounded by a chronic illness that limited her job options, but Fuentes soon learned what it was like to have her life governed by the platform’s algorithms that ensured the cheapest job dispatch for the workforce of more than a million Appen.

“We all help each other,” Fuentes told MIT about the support these workers gave each other to share what little work was available.

The MIT story recounts pay cuts, desperation to fill the dwindling work available, and account suspensions, which also triggered limited recourse to human operator pay suspensions from the platforms.

“What started in Venezuela created an expectation among AI industry players about how little they should pay for such services, and created a playbook for how to meet prices that customers trust,” says the history of MIT. .

During Venezuela's economic crisis, its currency was declared worthless and money filled the streets.

During Venezuela’s economic crisis, its currency was declared worthless and money filled the streets. Credit:access point

While data tagging has provided a lifeline to workers like Anaya, it has also exposed them to a Darwinian scale of exploitation as platforms cut their wages and suspended accounts — and livelihoods — in a continual race to the bottom.

The dangers include harsh criticism from customers that can lead to account suspension, and ambiguous tasks and clerical errors that can get an account suspended for months.

Julian Posada, a Yale associate professor 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 scour the world for cheap labor to perform these menial tasks.

But Venezuela’s educated population, great infrastructure before the collapse of its oil economy, provided a rare combination of ingredients that made it perfect for these subcontractors, says Posada.

“So on the one hand, you have the infrastructure to work with. On the other hand, you have people who are in a crisis with the highest levels of inflation, so you can pay them as low as possible,” says Posada.

At first, it was a good job.

To build a viable network of collaborators, these platforms offered bonuses and, in one case, even paid these outsourced workers an hourly rate. But once they reached critical mass, many of these payouts disappeared and payout rates fell.

In one case, a platform Posada studied accidentally left pay data for thousands of workers in a public Google spreadsheet.

He says it provided a clear picture of the relationship between rising crowds and falling wages.

“The more people joined, the fewer people won,” he says.

As the situation slowly improves in Venezuela, with higher oil prices, the trick will be finding the next low-cost labor market with enough people desperate for work.

“The next time there is a country in crisis, they will probably be there, as long as there are computers and desperate people,” says Posada.

After the MIT story, Appen began highlighting its treatment of its collaborative workforce, which includes the company’s code of ethics.

He cited an internal survey of 7,000 workers from late last year indicating that 17 per cent were long-term unemployed before joining Appen, 16 per cent living below the global poverty line. Sixty-three percent used Appen earnings to support their household or pay for education.

But another figure was telling. In its annual report, Appen reported that the survey showed that 67 percent identified Appen as their main source of income.

In response to queries, Appen said: “We are committed to fair pay and ethical treatment for our crowd. Our crowd code of ethics explicitly states that we aim to pay our crowd above the minimum wage in every market in the world in which we operate. To help guide our customers, we have a fair payment feature available on our platform.”

Appen also adjusts your task pay to the worker’s local minimum wage. This means that workers from a poor country are paid less for doing the same task than someone from a richer country. In the MIT story, Appen said he had seen an increase in fraud where users had used VPNs to access a higher paying offer in other countries.

Braden-Harder, for his part, is unimpressed with the talk of the minimum wage being set by individual states in the US and it tends to be really low.

“You can pay the legal minimum wage and still pay poverty wages,” he says.

Posada cited a recent fair labor project that investigated working conditions across all crowdsourcing platforms and found that none of them met minimum standards. But Appen was the best of a bunch of bad guys.

“It’s like, the best of the worst. They have some standards, they have some set rules,” she says.

Braden-Harder has retired from executive roles and is currently a member of the advisory board of the Institute for Global Social Benefits at Santa Clara University.

She helps advise global start-ups like one run in Kenya by an Australian college graduate who provides school lunches.

“I think all of us, myself included, believe that companies can do things for the better, but you have to have the right business model,” she says.

When it comes to solving the crowdsourcing problem, Braden-Harder says that large companies need to change their thinking when it comes to acquiring these services.

“In my experience, acquisitions are the downside of any company because the same guy who buys toilet paper for big companies also buys these services.”

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