The past year’s scandals surrounding Uber have shown us what a misdirected platform business is capable of discriminating against workers. From a series of workplace sexual harassment to the surging commission on drivers, Uber demonstrated that software systems, through information asymmetry, are more than capable of affecting various segments of the labor market in unexpected and powerful ways. This phenomenon is not unique to Uber; as pointed out in the Ladies Vanish article, similar attempts to mask belaboring and tedious works of humans behind the sexy tech of “on-demand magic” are almost universal across the tech industry.
We previously talked about how today’s technology is becoming increasingly abstract, thus becoming more accessible but less understandable. While the more obvious abstraction happens on the code level, such as API, libraries, and GUI, the more consequential abstraction happens when we separate and hide human labor from the user-friendly front end of software. The Hidden Figures movie was based on the story of three female engineers at NASA in 1961 and their achievements that were mostly hidden due to gender and race discrimination. More than a half-century later, however, the concealment of undesirable and the unpowerful labor force is still very much existent, if not more systematic in the “marketplace” economy that on-demand apps have created.
Technology and automation have helped workers become more productive by completing tedious and repetitive tasks and have also created new jobs that are more relevant to fast-changing market demand. To dismiss technological progress as the main culprit of unemployment, therefore, seems naive. Yet as this article states, “It’s one of the dirty secrets of economics: technology progress does grow the economy and create wealth, but there is no economic law that says everyone will benefit.” The increasing abstraction of technology has created an information—and power—asymmetry at an unprecedented scale. When centralized platforms and marketplaces take control of this power while denying all legal responsibility for labor and safety of their non-employee contractors, the supply side becomes insignificant and incapable of intervening against unfair treatment.
So how do users intervene? Do we only hope for philanthropic CEOs and software engineers to reject profit maximization and to operate as social enterprises? If there can be alternative services we can use that provide not only competitive monetary and time but also social benefit, perhaps there can be an additional motivation for these platform businesses to consider the good for all stakeholders in a system. Juno, for example, is a ride-sharing company that launched in New York in 2016 and takes a ‘smaller cut off every ride: Just 10% compared to Uber’s 20% to 25%’, as part of a strategy to attract and retain happier drivers. By only accepting already trained Uber or Lyft drivers with high ratings and advertising mainly through word-of-mouth, the business is able to cut training and advertising costs that are reallocated directly to drivers’ commission. By sometime in May or June this year, almost every Lyft driver I met in New York had Juno app on display in their cars. Every driver was excited to share how much more empowering Juno is to drivers than other ride-hailing services. I wondered if the right intention and vision of a company are enough to prevent the abuse of information asymmetry. At the same time, the distributed networks perhaps can one day remove this trust from the equation to create a fairer labor system.