Wednesday 18 March 2026 18:12
A former Google Cloud executive has put AI hiring and its consequences under new scrutiny in US courts.
The testimony of unnamed former Big Tech employees revolves around how automated systems and agents increasingly shape hiring decisions, not at the final interview stage, but much earlier, when candidates are screened, ranked, and, in many cases, excluded.
Employers have spent years embedding software into the hiring process, from basic applicant tracking systems to more sophisticated models designed to assess ‘fit’ or predict performance. But what has changed is how much they rely on technology.
Most large companies now use some form of automation to screen candidates.
And in many cases, these systems leverage historical data such as CVs and hiring results, to identify what a “successful” candidate looks like in their eyes.
The risk, increasingly flagged by regulators and researchers, is that these patterns may be reproduced, rather than challenged or rebuked.
This dynamic has sparked an increase in the number of legal cases in the US, where claims related to AI recruiting tools have increased since around 2022, the year ChatGPT launched.
Fear of bias in the system
Unlike typical hiring disputes, these cases are more difficult to prove because there is often no clear decision maker, no moment of interrogation, just a series of automated judgments that determine the final outcome.
Recruiting at scale is expensive and inconsistent, and automation offers speed and a way to manage volume, especially when the number of applications is increasing and recruiting teams are still under pressure.
But that efficiency comes with reduced transparency, with many new systems operating like what one legal expert described as a “black box with consequences,” producing outcomes that are difficult to explain simply.
This creates challenges not only for regulators, but also for companies trying to justify their own processes.
An example from recent years is Amazon, which abandoned its internal recruiting tool after it was found to prefer male candidates, reflecting its trained data.
More recent research suggests bias in CV screening systems and language models used in recruitment workflows.
Adoption shows no signs of slowing
AI is now shaping both sides of the hiring process. Employers use it to screen and assess candidates, while applicants use it to write CVs, prepare for interviews and optimize their profiles.
In many cases, automated systems evaluate candidates before recruiters review their applications.
More than a million jobs were cut in the US last year, even as companies rebuilt teams based on automation and new workflows.
The hiring process continues to evolve alongside these changes, with AI increasingly embedded at the starting point.
Regulations are also starting to respond, but not evenly. New York City now requires bias audits for automated recruiting tools, while other jurisdictions are still developing frameworks.
But in the UK and Europe, policy discussions are ongoing, with a focus on transparency and accountability.
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