AI and job applicants: how AI is changing hiring on both sides

AI and job applicants now meet at every step of hiring. Employers use AI to source, screen, test and interview candidates, and applicants use AI to write resumes, fill in applications and prepare for interviews. The result is faster hiring with new problems: floods of look-alike applications, a trust gap, and fresh legal duties for the teams that use AI to judge people.

TL;DR

• Employers use AI to screen, assess and interview candidates, while applicants use it to write resumes, apply in bulk and prepare for interviews.
• AI-generated applications make keyword screening less reliable, increase application volume and make it harder to verify candidates' actual skills.
• AI can improve hiring speed, consistency and access to skills-based opportunities, but biased algorithms, weak screening signals and poor transparency can undermine fairness.
• Candidate trust remains low. Hiring teams should disclose AI use, explain how decisions are made and offer a way to request human review.
• NYC and Illinois have AI hiring notice and fairness requirements in force. The EU AI Act's relevant hiring rules are scheduled for December 2027.
• Screen for demonstrated skills using role-specific assessments and structured interviews, protect assessments against cheating, and keep humans responsible for hiring decisions.
• Audit tools for bias, monitor pass rates across candidate groups and use job-related criteria rather than facial expressions, accents or other irrelevant signals.

How are AI and job applicants reshaping hiring?

AI and job applicants are reshaping hiring by putting AI on both sides of every step. Employers use AI to find, rank, test and interview candidates. Applicants use AI to write resumes, tailor cover letters, apply in bulk and rehearse answers. Hiring gets faster, but resumes look more alike and teams need better evidence of real skill.

Employer adoption is now the norm. In the Resume Genius Hiring Trends Report 2026, 87% of 1,500 US hiring managers said their company uses AI in at least one part of recruiting, and 58% use it to screen resumes and applications, up from 35% a year earlier.

The graph below shows where the two sides meet and what that means for the hiring team.

For a wider view of the employer side, see our guide to AI in the hiring process.

How are job applicants using AI in 2026?

Job applicants use AI mostly to write and polish application materials, and a growing share use it during assessments and interviews. Resumes and cover letters come first, followed by bulk applying, interview practice and, at the edge, live answer help and fake identities.

The numbers from recent surveys:

• Applying with AI: A Gartner candidate survey of 3,290 candidates found 39% used AI during the application process, mostly for resumes, cover letters, writing samples and assessment answers.
• What hiring managers see: In the Resume Genius 2026 report, 81% of hiring managers had come across candidate AI use, most often in resumes and cover letters (58%). Heavily AI-written materials were the second most common red flag (49%), behind job-hopping.
• Volume: LinkedIn sees about 11,000 applications a minute, up 45% in a year, as eWeek reported from The New York Times. Generative AI makes it easy to send a tailored resume to dozens of roles in an afternoon.
• Interviews and identity: 17% of hiring managers in the Resume Genius survey had met deepfake use in a video interview, and 6% of candidates in Gartner's research admitted fraud such as impersonation or using a proxy. Gartner predicts that by 2028 one in four candidate profiles worldwide will be fake.

Most applicant AI use is ordinary: fixing grammar, matching the language of a job ad, practising answers. The problem for hiring teams is not that candidates use AI. The problem is that a resume no longer proves much on its own.

What happens when both sides use AI?

When both sides use AI, hiring turns into what recruiter and writer Hung Lee called "an AI versus AI type of situation". Applicants use AI to beat filters, employers tighten filters with more AI, and the signal in each application gets weaker.

Three effects show up first:

1. Keyword screening stops working: A resume rewritten by AI to mirror the job description passes a keyword filter by design. Strong candidates who write their own resumes can rank below weaker ones who used a tool.
2. Recruiters drown in volume: More applications per role means less time per candidate, which pushes teams toward even more automation and less human review.
3. Trust drops on both sides: Employers doubt what they read, and applicants doubt that a person ever looked at their application.

The way out is to change what gets measured. If the resume is easy to fake, the evidence has to come from somewhere harder to fake: a skills test, a work sample or a structured interview, with checks that the candidate did the work.

Key takeaway: Treat an AI-polished resume as a first filter, not proof. Move the real decision to evidence of skill collected under fair, consistent conditions.

What are the benefits of AI in hiring for job applicants?

AI in hiring benefits job applicants when it is used to judge skills consistently and to answer them faster. Done well, AI shortens the wait, puts every candidate through the same questions and opens doors for people without the usual degree or job title.

• Faster answers: Automated screening and scheduling cut the weeks of silence many applicants complain about. A clear yes or no, even an automated one, beats being ghosted.
• The same test for everyone: A structured assessment or AI interview asks each candidate the same questions and scores them against the same rubric, which helps reduce the effect of one interviewer's mood or bias.
• Skills over pedigree: Skills tests let people show what they can do, which helps career changers, self-taught candidates and people from less known schools.
• Flexible timing: One-way video and on-demand tests let applicants take part outside working hours and across time zones.
• Better preparation: Applicants can use AI to understand a role, practise answers and fix their writing, which levels the field for people without career coaches.

What are the risks of AI for job applicants?

The risks of AI for job applicants come from tools that judge people on weak signals, learn bias from past hiring, or decide without explanation. Most of these risks are design and governance choices, which means hiring teams can reduce them.

• Learned bias: AI trained on past hiring repeats past patterns. As MIT Sloan describes, Amazon dropped a recruiting tool after it marked down resumes that mentioned "women's", as in "women's chess club captain". Rankings that look neutral can be harder to question than a manager's opinion.
• Keyword filters: Screening on exact terms rejects people who have the skill but describe it differently, and rewards people who copy the job ad.
• Signals unrelated to the job: Tools that score tone of voice, facial expressions or gaze raise privacy and fairness concerns, and can disadvantage candidates with disabilities or accents.
• No explanation and no appeal: Applicants rarely learn that AI was involved, what it looked at, or how to ask for a human review.
• Privacy: Video, audio and assessment data are personal data. Applicants deserve to know what is stored, for how long and who sees it.

For a deeper look at the ethics, read the ethical side of AI in recruitment and our guide to overcoming unconscious bias in hiring.

Why do job applicants distrust AI in hiring?

Job applicants distrust AI in hiring because they suspect it is screening them, cannot see how it decides, and rarely get told when it is used. Gartner found that only 26% of applicants trust AI to evaluate them fairly, while 52% believe AI already reviews their application data.

Employers add to the gap: In the Resume Genius 2026 survey, only 35% of hiring managers said their company always tells candidates when AI is used to evaluate them, and 20% said AI is used with no disclosure at all.

Distrust has a cost: Gartner reported that the share of candidates accepting job offers fell from 74% in Q2 2023 to 51% in Q2 2025. Not all of that drop is about AI, but candidates who feel judged by a black box are less likely to stay in the process or say yes at the end.

Pro tip: Add one plain sentence to every job ad and assessment invite: what AI does in your process, what a person reviews, and how to ask for an accommodation or a human review. It costs nothing and answers the question most applicants are already asking.

What laws protect job applicants from AI hiring tools in 2026?

Laws that protect job applicants from AI hiring tools now apply in New York City and Illinois, and the EU AI Act will cover hiring AI from December 2027. The common threads are notice to candidates, checks for bias, and human oversight.

Sources: NYC Department of Consumer and Worker Protection, Fisher Phillips on the Illinois amendment, and Cuatrecasas on the EU Digital Omnibus on AI, which reports the new EU date set by Regulation (EU) 2026/1744. Other US states have passed or proposed their own rules, so check with counsel for every location you hire in. This section is general information, not legal advice.

How should hiring teams respond to applicants using AI?

Hiring teams should respond to applicants using AI by changing what they measure, not by banning AI outright. Set a clear policy, move the decision from the resume to tested skills, check identity and integrity where it matters, and keep a person in charge of the final call.

1. Write a candidate AI-use policy: Say what is fine (polishing a resume, practising interviews) and what is not (live AI help during a test or interview, someone else taking it). Put it in the job ad and the test invite.
2. Screen on skills, not keywords: Replace keyword filters with a short, role-specific test early in the funnel. A resume can be written by AI; a timed coding task, a customer email or a spreadsheet exercise shows what the person can do. Browse role-based options in the Testlify test library.
3. Protect the test: Use proctoring that fits the role: full-screen mode, tab and copy-paste tracking, webcam snapshots, AI-assistance detection and ID checks for high-stakes roles. Our guide on how to prevent cheating in online interviews covers the options.
4. Structure the interview: Ask every candidate the same questions, score against a rubric, and add follow-ups about real past work that are hard to answer from a script. See what AI interviews are and whether they work.
5. Keep humans on the decision: Let AI rank and summarize, and let people review borderline cases, rejections at scale and final offers.

How can recruiters keep AI hiring fair for job applicants?

Recruiters keep AI hiring fair for job applicants by checking the tools, telling candidates what happens, and measuring outcomes by group. Fairness is not a feature you buy once; it is a routine you run.

• Ask vendors for evidence: Request bias audit results, what data the model uses, and how scores are explained to recruiters.
• Score job-related signals only: Prefer tools that score answers and work samples against the job, and avoid scoring appearance, facial expressions or accent.
• Watch pass rates: Compare pass rates at each stage by gender, age band and other groups where the law allows, and investigate gaps.
• Disclose and offer a route to a human: Tell applicants when AI is used and how to ask for an accommodation or a review.
• Train the team: Recruiters should know what the AI does, where it fails and when to override it.
• Write it down: Keep a short record of which tools you use, for which roles and decisions, and when they were last audited.

How does Testlify help hiring teams handle AI on both sides?

Testlify helps hiring teams handle AI on both sides by moving screening from resumes to skills, with integrity checks built in and people in charge of decisions. Teams can turn a job description into a role-specific assessment, run structured AI interviews and get explainable scores.

• Skills evidence, not keywords. 3,500+ tests and 25+ question types, from coding and spreadsheets to situational judgment and typing, so candidates show the skill instead of describing it.
• Explainable screening. The AI resume screener gives each applicant a match score with High, Medium or Low fit labels and the reasons behind it, and syncs results back to the ATS.
• Integrity built in. 20+ proctoring checks, including AI-assistance detection, copy-paste tracking and speech authenticity detection for voice and video answers, plus optional photo ID verification with face match. See anti-cheating and proctoring.
• Audited and global. Testlify's matching model completed a third-party bias audit under NYC Local Law 144 on June 16, 2023 (read the bias audit), and assessments run in 15+ languages.

To see how a skills-first flow would work for your roles, book a demo or start with a free trial.

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