AI hiring tools screening a job candidate before an interview

AI Hiring Can Reject You Before You Ever Get the Interview

Published: August 10, 2026

By Aaron Lazor

AI hiring tools can overlook qualified candidates when name variations, transliteration, and identity-matching errors distort the information used during recruitment.

Someone I know recently went through the interview process for a new job. He has a legitimate professional history and an established public profile, but most of it appears online under the English transliteration of his Hebrew name.

When the employer searched for him using his name in Hebrew, that professional history did not appear. According to the candidate, the missing information was treated as a red flag, and he did not get the job.

His first question to me was whether he could build his personal brand overnight.

The honest answer was no. But I also believe he was asking the wrong question.

He already had a professional history. The immediate problem was that the AI-assisted search did not connect it to the version of his name being evaluated.

AI Hiring Tools Are Changing The Job Search

Job seekers already suspect that automated systems are screening them out before they ever reach a recruiter. A recent Boston.com article looked at whether AI hiring tools are contributing to candidates being rejected or ignored during the job search.

The recruiters interviewed for that article didn’t blame AI for every rejection. The job market remains a major factor. But employers are using AI recruitment tools and applicant tracking systems to organize, filter, and rank large numbers of applications.

An AI system doesn’t have to make the final decision to change the result. It can influence which resumes receive attention, which candidates move forward, and which applications never reach a person.

Most of the discussion focuses on resumes. Did the candidate use the right keywords? Was the application formatted so the software could read it? Did the experience match the job description?

That’s not what happened here. This candidate had already entered the interview process. The concern reportedly arose when the employer used AI-assisted research to look beyond his resume and couldn’t find the professional footprint it expected.

I don’t know which AI recruitment software was used or how much weight the employer gave its output. But the experience raises a basic question that applies to any AI talent acquisition platform or general-purpose AI tool used during recruitment: Did it find the right person before it began evaluating him?

Why The Search Can Fail

AI doesn’t start with certainty. It starts with signals such as a name, location, employer, profession, school, website, or social profile. It uses those signals to retrieve information and estimate which sources belong to the person being searched.

Different tools do this differently. They may rely on conventional search, databases, knowledge graphs, language models, similarity scoring, or a combination of them. There isn’t one formula behind every AI hiring tool.

But the underlying problem is easy to understand. A close match is not necessarily the same person, and the same person is not always a close textual match.

Consider a name written in Hebrew and its English transliteration. There may be several acceptable English spellings, and the characters don’t directly match those in the original alphabet. Unless the system has enough supporting information to connect the two versions, it may treat them as separate identities.

The reverse can happen just as easily. Two people may have nearly identical names, work in similar fields, or share another common detail. A system can decide that information about one of them probably belongs to the other.

The final response may still sound clear and confident. What the reader usually can’t see is what the system failed to retrieve, which spellings it considered, or how certain it was that all the sources belonged to the same person.

This isn’t just a theoretical risk.

The Jeffery Battle Record

Jeffery Battle alleged in Battle v. Microsoft Corporation that Microsoft Bing and Bing Chat combined his professional information with criminal and terrorism-related information concerning another man, Jeffrey Leon Battle.

According to the complaint, the generated material described Jeffery Battle as an author, professor, journalist, business executive, and U.S. Air Force veteran. It also associated him with another man’s conviction for seditious conspiracy and levying war against the United States.

SecondSideMedia published a Factual Clarification concerning Jeffery Battle and the alleged AI identity conflation. The record doesn’t decide whether Microsoft is liable, and the allegations haven’t been decided on their merits. In October 2024, the federal court compelled arbitration and stayed the case.

What interests me here is the identity problem itself. Battle alleges that AI connected information belonging to different people. The job candidate faced the other side of that problem: information belonging to him wasn’t connected at all.

We’ve also observed an AI-generated business narrative that appeared to draw from several separate companies with similar names. The sources themselves weren’t necessarily invented. The problem was that the output didn’t reliably preserve which information belonged to which company.

That distinction matters because the phrase “AI hallucination” can be too narrow. AI doesn’t need to invent a fact to produce a misleading narrative. It can retrieve a real fact and attach it to the wrong identity, mix several identities together, or overlook information that should have been included.

A Polished Answer Can Still Be Incomplete

Before generative AI, an empty search result might have prompted someone to try a different spelling, another language, or another database. Generative AI changes the experience because it turns whatever was retrieved into a polished answer.

That answer can look complete even when the search behind it wasn’t.

If little appears, an employer may conclude that the candidate has no meaningful professional footprint. If adverse information appears, the employer may assume the identity has already been checked. The candidate may never know what was searched, what was found, or that an AI-generated summary influenced the decision.

Talent acquisition teams need to account for that limitation. If AI candidate screening or AI-assisted research produces information that could affect a hiring decision, a human reviewer should verify the name, reasonable spelling variations, language differences, location, employers, and the underlying sources. The candidate should also have a meaningful opportunity to explain information that may be incomplete or wrong.

AI recruitment software can make hiring more efficient. It can’t turn a probable identity match into a verified one.

He Didn’t Need A Personal Brand By Tomorrow

The candidate’s question stayed with me because it exposes a gap between two different types of work.

A PR firm can help a person or business build visibility and credibility. That may include media coverage, thought leadership, interviews, professional positioning, social content, and other authoritative material. Done properly, that work takes time. You can’t build a genuine reputation overnight.

But this candidate wasn’t starting from nothing. He had already built a professional identity. His problem was that the system appeared unable to connect the English and Hebrew versions of it.

That’s closer to the layer SecondSideMedia works on. We look for specific gaps, conflation, or missing context in AI-generated narratives. We then publish attributable records that make the relevant identity, distinction, response, or supporting fact explicit.

A published record isn’t a substitute for a broader PR strategy, and it won’t automatically change every AI output. Likewise, PR work doesn’t necessarily correct the way an AI system connects existing sources or distinguishes between similar identities. These are different jobs, and there are situations where a client needs both.

That is why I see SecondSideMedia as a complementary layer for PR and crisis-management firms. A firm’s reputation strategy may be working in traditional media, search, and public communications while an AI narrative remains incomplete or connects the wrong information. Finding that gap helps protect the work already being done for the client.

The Question Employers Should Ask

If an organization uses AI recruitment tools, AI recruitment software, or any other form of AI-assisted research, it shouldn’t rely on the summary before confirming the identity behind it.

The practical question is simple: Did the system find the right person, enough of that person’s actual history, and only information that belongs to that person?

For Jeffery Battle, the allegation is that someone else’s history became part of his. For the candidate I know, his own history effectively disappeared because it was built under another version of his name.

Neither problem is solved by a confident answer on a screen.

Employers should verify the identity and sources before allowing an AI-generated narrative to affect a candidate’s future. PR, reputation, and crisis-management firms should also review how AI systems identify and describe their clients, particularly when those clients use multiple names, spellings, languages, or business entities.

Otherwise, a qualified candidate can lose a job because the system never found the person who actually applied.

Supporting Sources