That Stanford AI Hiring Study Everyone's Talking about: What is 'AI Algorithmic Hiring' & What Do You Need to Know About It?
- Jun 25
- 3 min read
We are all hearing about the new and exciting ways that AI is being used on what feels like a daily basis. In the job market, AI is no longer just a productivity tool for job seekers and it is increasingly part of how companies are evaluating their candidates. Many candidates immediately think of applicant tracking systems (ATS), resume keywords, and getting filtered out by a robot when they think about AI in hiring processes. In reality, it’s more nuanced than that. Companies are using AI across all stages of hiring, including application volume management, assessments, and candidate evaluation.
While AI is not replacing recruiters, it is changing how candidates are discovered, evaluated, and compared to each other. Recent research from Stanford’s Digital Economy Lab examined a growing concern around this trend: what happens when several different employers rely on the same (or similar) AI-driven hiring systems to make their decisions?
Simply put, hiring teams are using AI-assisted tooling to help manage scale and consistency in their hiring systems. That can look like…
Resume parsing and candidate organization
Matching candidates to skills or job requirements
Assessments designed to evaluate traits, skills, or fit
Supporting structured interview processes
Helping recruiters review large candidate pools
Harver, who acquired Pymetrics (a leading AI-driven hiring algorithm provider frequently referenced in this study and resulting articles), designs their tools and products around high-volume hiring, assessments, and professional hiring workflows where AI-assisted evaluation can help employers process more candidates and standardize parts of the process. These tools are typically intended to be used to help employers organize information, identify patterns, and honor both candidates and their company by moving with speed. Most hiring teams are not using AI to simply make a final “hire/no hire” decision by itself. But how are these types of tools actually impacting candidates?
What the Stanford AI hiring study illuminated was what actually happens behind the scenes when many companies rely on the same or similar algorithmic approaches or tools for evaluating their candidates. They found that there are clear negative impacts to underrepresented/marginalized groups, despite many of these AI hiring tools self-promoting as being built and trained to mitigate bias in the hiring process. Beyond that, the study demonstrated, using real hiring data from 4 million job applications to 156 employers, that if many companies use similar AI systems to evaluate candidates, they may begin looking for the same signals of “fit.” This can shape which candidates are surfaced, evaluated, and ultimately considered across the hiring landscape.
The research highlighted that these algorithmic hiring systems can produce different outcomes depending on:
The data they are trained on
How they are designed
How they are audited
How humans interpret their outputs
As a result, as pointed out in Fortune’s article by Nick Lichtenberg, when companies inevitably use the same vendor as each other, these algorithms become “so highly correlated across employers that being rejected by one company meaningfully predicts rejection by the next” in a pattern of systemic rejection. The Stanford study found that these patterns lead to statistically higher rejection rates than if employers were making independent hiring decisions. Harver’s tools often also store these candidates scores/ratings for up to 330 days, reusing a candidate’s score rather than re-evaluating a candidate applying for another job or another employer in their system.
This new normal of hiring is understandably daunting for jobseekers. The takeaway, though, is not to “learn how to trick AI”. Candidates need to instead re-calibrate to what signals are becoming most important to hiring teams and their systems. As AI makes it easier to generate polished resumes and applications at scale, hiring teams are looking more closely for evidence of:
Specific experience
Measurable impact
Clear communication
Problem-solving ability
Alignment between your background and the role
AI is also changing and evolving at all times, so truly the strongest candidates are the ones who make it easier for both AI-assisted tools and human decision-makers to understand their value.
It’s not time to panic. The fundamentals of standing out as a candidate have not disappeared, even with AI evolving how the process looks. The future of hiring shouldn’t be humans versus AI; hiring should always be driven by candidates who understand the process as it evolves and know how to show up clearly in that process. Candidates who understand how hiring systems work can use that knowledge strategically to…
Build clearer positioning
Apply with more intention
Prepare stronger examples
Communicate their experience more effectively
That’s also where Top of the Stack can be your catalyst to get on hiring teams’ radar. We help professionals clarify their positioning, strengthen their candidate assets, and succinctly communicate their value in this changing hiring landscape. If you’re ready to stop spinning your wheels and start getting traction with hiring teams, explore our services Here.
