Platform Work - The algorithmic gamblification of on demand gig work - February 8, 2023

MARTIJN ARETS (LinkedIn - Twitter)

In the early days of Uber, everyone was excited about the 'surge pricing' the company uses. If there is more demand than supply somewhere at a given time, prices rise. With this, demand goes down and supply goes up. At the time, many saw this as a perfect economic model of flexible pricing. The example of the hairdresser was often brought to mind: why do you pay the same for a haircut on Tuesday afternoon as on Friday evening, when demand is many times higher? Now it appears (and this is not overnight) that these 'smart' (or: 'savvy') technologies are able to root for and entice working people to do more than initially envisaged.

In the article "The house always wins: the algorithmic gamblification of work", scientist Veena Dubal gives an interesting (and shocking) insight into the algorithms used by Uber to direct workers to be available as much as possible at the platform's convenience. This is also because the risk of not working is at the worker's expense: something that, in my opinion, is a very bad idea anyway.

THE ALGORITHMIC WAGE DISCRIMINATION

In this article:

“In a new article, I draw on a multi-year, first-of-its-kind ethnographic study of organizing on-demand workers to examine these dramatic changes in wage calculation, coordination, and distribution: the use of granular data to produce unpredictable, variable, and personalized pay. Rooted in worker on-the-job experiences, I construct a novel framework to understand the ascent of digitalized variable pay practices, or the transferal of price discrimination from the consumer to the labor context, what I identify as algorithmic wage discrimination. As a wage-setting technique, algorithmic wage discrimination encompasses not only digitalized payment for work completed, but critically, digitalized decisions to allocate work and judge worker behavior, which are significant determinants of firm control.

Though firms have relied upon performance-based variable pay for some time, my research in the on-demand ride hail industry suggests that algorithmic wage discrimination raises a new and distinctive set of concerns. In contrast to more traditional forms of variable pay like commissions, algorithmic wage discrimination arises from (and functions akin to) to the practice of consumer price discrimination, in which individual consumers are charged as much as a firm determines they are willing to pay.

As a labor management practice, algorithmic wage discrimination allows firms to personalize and differentiate wages for workers in ways unknown to them, paying them to behave in ways that the firm desires, perhaps for as little as the system determines that they may be willing to accept. Given the information asymmetry between workers and the firm, companies can calculate the exact wage rates necessary to incentivize desired behaviors, while workers can only guess as to why they make what they do.”

You don't have to be an activist to understand that such techniques are far from desirable. This piece includes the experiences of some drivers:

“Domingo, the longtime driver whose experience began this post, felt like over time, he was being tricked into working longer and longer, for less and less. As he saw it, Uber was not keeping its side of the bargain. He had worked hard to reach his quest and attain his $100 bonus, but he found that the algorithm was using that fact against him.”

COMPLEXITY AND TRANSPARENCY

I think it is important to let platforms take more responsibility in explaining their processes and having this validated by a trusted third party. The fact that platforms like Uber frame complexity as an added value for the worker is evident from this quote:

“If you joined Uber years ago, you will have joined when prices were quite simple. We set prices based on time and distance and then surge helped increase the price when demand was highest. Uber has come a long way since then, and we now have advanced technology that uses years of data and learning to find a competitive price for the time of day, location and distance of the trip.”

When someone takes pride in adding complexity, it should lead to suspicion by default. Because complexity can also be used to hide things. I wonder if upcoming European regulations will lead to less complexity and unclear processes for the worker. It should be a key issue for policymakers anyway.