has highly paid executives and researchers at the top, and at the bottom, working stiffs who toil at screens training algorithms.
Workers often have to take second jobs or night shifts, says Madhumita Murgia, the AI editor of the Financial Times whose recent book Code Dependent features their stories from across the developing world. One woman who worked for Samasource Impact Sourcing in Nairobi, for instance, couldn’t support herself and her daughter on her salary and had to move in with her parents, Murgia says.
Leaders of data-labelling firms often start with noble intentions to help pull people out of poverty, but they’ve struggled to get corporate customers to pay higher rates as competition in their field has increased. As such, most data work platforms don’t have policies in place to ensure their workers earn at least the local minimum wage, according to a 2021 survey from the Oxford Internet Institute.
The company and rivals like San Francisco-based Samasource Impact Sourcing, Argentina’s Arbusta S.R.L. and Bulgaria’s Humans in the Loop play a critical role in the AI supply chain, but for years now have typically paid just enough for workers to maintain a living, Murgia and Dr Miceli say. Perhaps we can learn something from Nike. Back in the 1990’s, the company faced an enormous backlash for the long hours and meagre wages its workers in developing nations earned. Over time, consumer boycotts and pressure from the media led Nike to put in stricter labour policies. It spent millions of dollars on improving conditions and pay.