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Pay is personal for plenty of Americans, but a new distribution model that consumes vast quantities of worker data is turning pay into something else: personalized.
For an increasing number of workers in America, the money they can expect to be paid on any given day, week, or month is unknown to them. They could work the same number of hours as they did the shift before. They could help the same number of customers. They could do everything, as nearly similar as possible, and still be paid less than another worker in the exact same position, or even themselves just last week.
The mechanism behind this pay disparity is called algorithmic wage discrimination and while the term may be new, it’s inner workings could sound quite familiar.
Algorithmic wage discrimination describes the zig-zag pay that is meted out to contract workers by big companies like Uber and Amazon. Whereas many workers in the world rely on salaries, or commissions, or self-determined contract rates, workers at Uber are different.
In the same way that Uber decides what you pay for a ride to the airport, Uber also decides what a driver makes. And the calculus behind that decision is opaque. Location, traffic, the time of day, and the number of drivers on the road all play some role, but not a complete one. And in the same way that Uber incentivizes you with a flash sale or a price so high that you maybe walk a couple blocks in a different direction to get a lower price, Uber incentivizes drivers with bonuses and challenges, keeping them on the road perhaps longer than they intended.
The end result, then, isn’t just unpredictable pay—it’s potentially an attempt to predict and control behavior.
For her 2023 paper, titled “On Algorithmic Wage Discrimination,” professor of law Veena Dubal spoke with many Uber drives who compared this system to “casino culture,” in that the pay is unpredictable but the potential for a jackpot—or, just a good payment on one ride—is enough to convince drivers to stick around, night after night, hour after hour.
As one driver told Dubal:
“It’s like gambling! The house always wins.”
Today, on the Lock and Code podcast with host David Ruiz, we speak with Dubal—professor of law at the UC Irvine School of Law—about how algorithmic wage discrimination works, what data it consumes to function, and the threat it poses as it creeps from gig work into many more industries.
Tune in today.
You can also find us on Apple Podcasts, Spotify, and whatever preferred podcast platform you use.
For all our cybersecurity coverage, visit Malwarebytes Labs at malwarebytes.com/blog.
Show notes and credits:
Intro Music: “Spellbound” by Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0 License
http://creativecommons.org/licenses/by/4.0/
Outro Music: “Good God” by Wowa (unminus.com)
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