Research shows that an 'algorithmic monopoly culture'—resulting from over 90% of companies using a small number of AI hiring tools—is excluding certain job seekers from the labor market and fostering discrimination.
Imagine this: You have worked hard to build your career and have sent your prepared resume to several places. But all you get in return is an automated response email that ends with a single click: “We regret to inform you that your qualifications…” Why is this happening? What if it’s not because you lack ability, but because every company is using the “same AI interviewer”?
Recent large-scale research analyzing 3.4 million job seekers and 4 million applications reveals a dark side of our hiring market: the invisible wall known as “algorithmic monoculture.” Reference 2, Reference 9
Why Is This Important?
Simply having a machine review documents might not be the problem itself. However, the core issue is that 90% of US companies use AI algorithms in their hiring process, and the vast majority of them share tools created by a small number of vendors. Reference 10
What happens if these few AI hiring tools judge only people with certain criteria as “excellent talent”? Job seekers who do not fit those criteria will be turned away for the exact same reason by every company they apply to. This becomes a structural discrimination created by technology, rather than individual bad luck. Researchers point out that this environment can lead to “systemic rejection,” where certain individuals or racial groups are repeatedly rejected. Reference 1, Reference 4
Easy Understanding: The Rise of the ‘Carbon Copy Interviewer’
To put it simply, think of “algorithmic monoculture” like this: Imagine there are 100 companies, but they have all granted hiring authority to a single interviewer. If that interviewer only likes a certain style of person, you could be rejected 100 times just because you don’t fit that style, no matter how talented you are.
Professor Sarah Bana defined this as “all situations where similar outcomes occur because of algorithms.” Reference 3 While simple criteria like degrees or experience played this role in the past, machines are now “learning” and automating those criteria. AI is efficient, but if everyone is looking at the same AI, the diversity of the hiring market disappears in an instant.
Current Situation: Reality Before Our Eyes
This technology has already become deeply embedded in our lives. Even federal government agencies are utilizing algorithms from companies like HireVue for hiring. Reference 5 The problem is that these efficient tools are showing actual bias.
Research indicates that racial bias and systemic rejection issues are becoming reality, such as Black or Asian job seekers experiencing disadvantages under these algorithmic systems. Reference 10 Companies introduced AI to save time and money, but they may ultimately be losing the essence of hiring: “fair opportunity.” Reference 7
Where Do We Stand?
We currently stand between the sweet fruits of technological efficiency and the bitter reality of the inequality it causes. When first introduced, AI hiring systems were expected to reduce recruiter bias and provide more objective evaluations. In reality, however, as everyone uses the same algorithm, it is being revealed that results once believed to be “objective” are actually collections of biases skewed in a specific direction. It is like every chef using the same soy sauce to cook; eventually, the taste of our table (the hiring market) becomes monotonous, and those allergic to that soy sauce lose their place.
What Happens Next?
Just as important as the speed of technological advancement is how technology changes society. When many people use the same algorithm, there is a risk that the judgments made by that system will solidify as if they were “absolute truth.”
We must ask more strictly in the future how AI hiring tools can ensure “fairness” beyond mere “efficiency.” Companies must transparently disclose what biases their algorithms may have, and it is time for job seekers to recognize the structural problems hidden behind AI-driven judgments and make their voices heard. The era is no longer about technology choosing people; it is time for us to choose and monitor whether technology is treating people properly.
References
- Algorithmic Monocultures in Hiring - Stanford Digital Economy Lab (https://digitaleconomy.stanford.edu/publication/algorithmic-monocultures-in-hiring/)
- Algorithmic Monocultures in Hiring (https://algorithmichiring.github.io/)
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Q&A Algorithmic Monoculture in Hiring - Stanford Digital Economy Lab (https://digitaleconomy.stanford.edu/news/qa-algorithmic-monoculture/) - [2605.27371] Algorithmic Monocultures in Hiring (https://arxiv.org/abs/2605.27371)
- Algorithmic Monocultures in Hiring RISHI BOMMASANI, Stanford University, USA (https://arxiv.org/pdf/2605.27371)
- AI Hiring Tools Can Yield Racial Bias and Systemic Rejection (https://hai.stanford.edu/news/ai-hiring-tools-can-yield-racial-bias-and-systemic-rejection)
- Algorithmic Monocultures in Hiring - catalyzex.com (https://www.catalyzex.com/paper/algorithmic-monocultures-in-hiring)
- Algorithmic Monocultures in Hiring: 90% of US Employers Share One Vendor (https://www.devdigest.org/articles/algorithmic-monocultures-in-hiring-90-of-us-employers-share-one-vendor)
- A phenomenon where all companies develop their own AI
- A state where many decision-makers rely on identical algorithmic recommendations
- Technology where AI makes hiring decisions on its own
- Lack of opportunities for direct interviews with recruiters
- Systemic rejection and discrimination due to algorithmic monoculture
- Resumes being omitted due to AI system errors
- About 30%
- About 60%
- About 90%