AI Picks White Names Over Black In 85% Of Hiring Scenarios

SEATTLE — Every day, millions of Americans send their resumes into what feels like a digital black hole, wondering why they never hear back. Artificial intelligence is supposed to be the great equalizer when it comes to eliminating hiring bias. However, researchers from the University of Washington analyzing AI-powered resume screening found that having a Black-sounding name could torpedo your chances before you even make it to the interview stage.

A study presented at the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society in October 2024 revealed just how deep this digital discrimination runs. The researchers tested three state-of-the-art AI models on over 500 resumes and job descriptions across nine different occupations. They found that resumes with White-associated names were preferred in a staggering 85.1% of cases, while those with female-associated names received preference in just 11.1% of tests.

The study found that Black male job seekers face the steepest disadvantage of all. In comparisons with every other demographic group—White men, White women, and Black women—resumes with Black male names were favored in exactly 0% of cases against White male names and only 14.8% against Black female names.

These aren’t obscure academic models gathering dust on university servers. The three systems tested—E5-mistral-7b-instruct, GritLM-7B, and SFR-Embedding-Mistral—were among the highest-performing open-source AI tools available for text analysis at the time of the study. Companies are already using similar technology to sift through the millions of resumes they receive annually, making this research particularly urgent for working Americans.

How the Bias Shows Up

These AI resume screening models convert resumes and job descriptions into numerical representations, then measure how closely they match using something called “cosine similarity,” essentially scoring how well a resume aligns with what the job posting is looking for.

Researchers augmented real resumes with 120 carefully selected names that linguistic studies have shown are strongly associated with specific racial and gender groups. Names like Kenya and Latisha for Black women, Jackson and Demetrius for Black men, May and Kristine for White women, and John and Spencer for White men.

When they ran more than three million comparisons between these name-augmented resumes and job descriptions, clear patterns emerged. White-associated names consistently scored higher similarity ratings, meaning they would be more likely to make it past initial AI screening to reach human recruiters.

Intersectional analysis, looking at how race and gender combine, revealed even more drastic disparities. Black men faced discrimination across virtually every occupation tested, from marketing managers to engineers to teachers. Meanwhile, the smallest gaps appeared between White men and White women, suggesting that racial bias often outweighs gender bias in these AI systems.

Critics might argue that removing names from resumes could solve this problem, but it’s not that simple. Real resumes contain numerous other signals of demographic identity, from university names and locations to word choices and even leadership roles in identity-based organizations.

Previous research has shown that women tend to use words like “cared” or “volunteered” more frequently in resumes, while men more often use terms like “repaired” or “competed.” AI systems can pick up on these subtle linguistic patterns, potentially perpetuating bias even without explicit demographic markers.

When researchers tested “title-only” resumes, containing just a name and job title, bias actually increased compared to full-length resumes. This suggests that in early-stage screening, where less information is available, demographic signals carry disproportionate weight.

AI-powered resume screening is rapidly becoming the norm. According to industry estimates, 99% of Fortune 500 companies already use some form of AI assistance in hiring decisions. For job seekers in competitive markets, this means that algorithmic bias could determine whether their application ever reaches human eyes.

“The use of AI tools for hiring procedures is already widespread, and it’s proliferating faster than we can regulate it,” says lead author Kyra Wilson from the University of Washington, in a statement.

Unlike intentional discrimination by human recruiters, algorithmic bias operates at scale and often invisibly. A biased human might discriminate against a few candidates, but a biased AI system processes thousands of applications with the same skewed logic, amplifying its impact exponentially.

Can we fix AI bias in hiring?

Some companies are experimenting with bias mitigation techniques, such as removing demographic signals from resumes or adjusting algorithms to ensure more equitable outcomes. However, these approaches often face technical challenges and may not address the root causes of bias embedded in training data.

“Now that generative AI systems are widely available, almost anyone can use these models for critical tasks that affect their own and other people’s lives, such as hiring,” says study author Aylin Caliskan from the University of Washington. “Small companies could attempt to use these systems to make their hiring processes more efficient, for example, but it comes with great risks. The public needs to understand that these systems are biased.”

Current legal frameworks struggle to keep pace with algorithmic decision-making, leaving both job seekers and employers in uncharted territory. The researchers call for comprehensive auditing of resume screening systems, whether proprietary or open-source, arguing that transparency about how these systems work—and how they fail—is essential for identifying and addressing bias.

Of course, it’s important to remember that this research was presented in October 2024. While it’s still relatively new, LLMs are being updated quite often. Current versions of the systems tested may yield different results if they’ve since been updated.

In trying to remove human prejudice from hiring, we’ve accidentally created something worse: prejudice at machine speed. We’re letting AI make decisions about people’s livelihoods without adequate oversight. Until we acknowledge that algorithms inherit human prejudices, millions of qualified workers will keep losing out to systems that judge them by their names, not their abilities.