Algorithms trained on payday-loan histories and credit-card balances are calibrating job offers to individual workers, and a first-of-its-kind audit has named major U.S. employers as customers of the tools that make it possible.
How the Data Gets Used
Before HR reads your resume, an algorithm may already have set the ceiling on your offer.
Buried in your credit history, your social-media activity, and your past pay acceptances is a number: the lowest salary you will likely take.
Researchers and labor advocates call the practice of using that data to set compensation “surveillance wages,” a direct extension of the surveillance app techniques companies have refined for years on retail consumers.
According to Nina DiSalvo of labor advocacy group Towards Justice, systems can ingest indicators such as payday-loan history or high credit-card balances to estimate a candidate’s wage sensitivity. Offers are then calibrated around perceived desperation rather than qualifications or market rates.
Some tools also scrape public social-media profiles to anticipate union sympathies or potential pregnancy, raising discrimination concerns that go beyond pay.
A first-of-its-kind audit of 500 labor-management AI vendors was published in August 2025 by law professor Veena Dubal and tech strategist Wilneida Negrón through the Washington Center for Equitable Growth. Their findings show employers across healthcare, customer service, logistics, and retail are customers of tools designed for this kind of algorithmic pay practice.
The audit identified Intuit, Salesforce, Colgate-Palmolive, Amwell, and Healthcare Services Group among those customers. It explicitly noted it could not confirm that every named employer actually uses these tools for wage surveillance.
Colgate-Palmolive stated it “does not use algorithmic wage-setting tools to make compensation decisions for our employees or to set new-hire salaries.” Intuit similarly said it “does not engage in such practices.”
Other companies flagged in the audit did not respond to questions about their methods.
Two Industries Where It’s Already Happening
Gig nurses and rideshare drivers are already living inside the algorithm.
A December 2024 Roosevelt Institute report, based on interviews with 29 gig nurses, found that on-demand staffing platforms including CareRev, Clipboard Health, ShiftKey, and ShiftMed use algorithms to set pay for individual shifts rather than fixed wages.
Nurses reported receiving different amounts for identical work at the same facility, with platforms learning from each acceptance to calibrate future offers.
The Roosevelt authors call this “algorithmic wage discrimination.” Their definition: workers paid different hourly amounts based on ever-changing calculations and informational asymmetries.
ShiftKey denied using surveillance-wage practices and stated it does not use data-broker services or debt data to set wages. CareRev, Clipboard Health, and ShiftMed did not respond to detailed questions.
Rideshare drivers have been navigating a similar reality for years. Los Angeles driver Ben Valdez, a member of Rideshare Drivers United, observed that different drivers were sometimes offered different base fares for the same trip at the same time, with a take-it-or-leave-it rate presented first and adjusted only after enough drivers reject it.
Legal scholar Zephyr Teachout puts it plainly: Uber “uses data-rich driver profiles to match the wage to the individual incentives of the driver and the needs of the platform.”
Uber disputes that characterization, stating its up-front fares are determined by time, distance, and demand conditions, and that its algorithms do not use individual driver characteristics or past behavior to set pay. Nicole Moore of Rideshare Drivers United describes the experience differently: the system is “judging our desperation rate.”
The Legislation That Almost Changed It
Colorado advanced the furthest toward banning surveillance-based wage setting, then its governor vetoed the effort.
Colorado lawmakers advanced two bills specifically targeting the practice. HB25-1264 defined “surveillance data” and prohibited its use in automated systems that set individualized prices or wages, treating violations as deceptive trade practices enforceable by the attorney general and private lawsuits.
A follow-on bill, HB26-1210, expanded the framework to require employers to disclose which data factors into wage decisions and to give workers mechanisms to access and correct that data. It passed the Colorado Senate and was sent to the governor.
In June 2026, the governor vetoed the ban, according to The Guardian, leaving the regulation unsettled despite Colorado’s early lead. New York adopted a narrower rule requiring companies to disclose when consumer prices are set algorithmically, but that measure covers retail transactions, not paychecks.
Without a federal floor or broader state action, the algorithm that sets your Uber fare may increasingly set your salary. Author Joe Hudicka describes the result as a “wage-surveillance ceiling.” Unlike the glass ceilings workers have historically fought to shatter, this one is invisible. Workers may never know which financial and legal data points locked them below it.
