By Cara Waters
Australian artificial intelligence company Appen has been hit by claims of racism in its recruitment processes after it asked job candidates to take a “paper bag test” about their skin colour.
A recruiter for Appen contacted Houston based Charné Graham to apply for a role as a social media evaluator at Appen. But after selecting ‘Black or African American’ from a drop down box on the online application she was then asked to select her complexion as Light - pale white, White- fair, Median - white to light brown, Olive - moderate brown, Brown - dark brown or Very dark brown to black.
Ms Graham posted a screen shot of the question to Twitter and tweeted “I did not continue with the [application] after seeing the paper bag test”.
Charné Graham was perplexed by the questions when she applied for a job at Appen.
A paper bag test is a term to describe a colourist discriminatory practice from the 20th century in which an individual’s skin tone was compared to the colour of a brown paper bag.
The test can be traced back to the time of slavery when slave masters kept lighter skinned slaves inside.
ASX listed Appen has a market capitalisation of $1.43 billion and makes money by crowdsourcing labour for artificial intelligence and machine learning services for tech giants such as Google and Facebook.
Ms Graham told The Age and The Sydney Morning Herald she had a background in social media and digital marketing so at first glance the position made sense to her.
“I later learned that the role would be remotely fulfilling random tasks at a very low pay rate,” she said. “None of these tasks/projects required my complexion.”
Ms Graham said she was perplexed by the question because it was the first time she had seen it as part of the optional identity portion of an application.
“I’m aware that Appen is an artificial intelligence company but as a Black woman the question is very off putting and triggering with no clear explanation as to why you would need that information,” she said. “After being unemployed for a year due to the pandemic, my job search has exposed me to lots of industries and companies across the globe. The reviews on Glassdoor for Appen are also very revealing about what type of company it is.”
The Appen controversy comes amid a growing conversation within the Australian technology sector about diversity, amid perceptions the industry remains dominated by white males.
A spokeswoman for Appen said the company wanted to apologise for the way the question was phrased.
“Our goal is to help eliminate bias and make AI that works for everyone,” she said. “The optional question on skin tone is used to ensure diverse datasets are included in the collection and annotation used to train computer vision algorithms.”
The spokeswoman said there was no intended racism in Appen’s hiring processes, practices or policies.
“We acknowledge that without an explanation up front as to why it is so important to ask some of these questions, and the way the question was presented, it missed the mark and that’s on us to fix,” she said.
Appen is one of the so-called WAAAX technology stocks on the ASX, along with WiseTech, Afterpay, Altium and Xero that have boomed in recent years.
It makes most of its money in the United States from crowdsourcing a global workforce that does the low-level grunt work for the technology giants of teaching computers to recognise basic images and speech.
However, Appen missed its most recent earnings targets after growing concern in the market that its heavyweight customers - Google, Facebook, Amazon and Microsoft - which generate 80 per cent of its revenue are becoming less reliant on its services.
Ms Graham said Appen’s questioning was a lazy way to try to diversify the artificial intelligence industry.
“This particular portion of the on-boarding process was a failed attempt of implementing diversity in any industry,” she said. “Diversity, equity and inclusion are vital in every company but since the tech industry has been historically known to exclude women and Black people this question was not the best way to test out ‘machine learning’.”