Increasingly automated job screening systems are forcing job seekers to adopt time-consuming tasks to be noticed, like writing application-specific resumes
because prejudice allowed them into those roles more easily—it will conclude that white males are the ‘best’ candidates.” https://www.vice.com/... @imani_barbarin : It's also ableist, because if you're only seeing candidates through the lens of technology, then disabled candidates will always be rooted out. https://twitter.com/... S.E. Smith / @sesmith : Applying for jobs isn't about putting your best foot forward. It's about getting through an algorithmic gauntlet so a human MIGHT get to see your resume. https://www.vice.com/... Taylor Lorenz / @taylorlorenz : Companies are increasingly using automated systems to select who gets ahead, who gets eliminated from pools of applicants. For jobseekers, this means bizarre time-consuming tasks demanded by companies who have not shown any meaningful consideration of them https://www.vice.com/...
Context & Ripple Effects
This piece lands a month after CNN reported that university career counselors were already coaching students on how to impress vetting algorithms, so the burden of machine-readable applications was visible before anyone named its costs. What the VICE reporting adds is who pays them: as S.E. Smith puts it, applying has become an algorithmic gauntlet where a human might never see the resume, and critics like Imani Barbarin flag that screening trained on past hires can privilege white male candidates while rooting out disabled ones.
First-order effects
- Job seekers now absorb unpaid labor per application — tailoring resumes to each posting — just to clear automated filters before any recruiter reads their materials.
- Disabled candidates and applicants whose profiles diverge from historically hired patterns face systematic exclusion when screening models learn from past hiring decisions.
Second-order effects
- Employers respond to degraded applicant pools with more automation, not less — the pattern that later produced employers overwhelmed by AI-generated résumés and turning to AI video-interviewing tools to rank candidates.
- Applicants escalate in kind with generative tools, feeding the arms race between employers and jobseekers that raises costs and friction on both sides without improving matches.
Third-order effects
- If both sides keep automating, hiring converges on algorithm-to-algorithm selection where bias encoded in training data compounds at scale, making discrimination harder to detect and audit than in human-screened processes.
- The screening layer becomes the de facto gatekeeper of labor markets, shifting power toward whichever vendor's ranking model employers adopt — and making the fairness of those opaque models a systemic rather than per-company question.
The trend: Hiring is locked in an escalating automation arms race — each side's AI forcing more elaborate countermeasures from the other, with bias and exclusion compounding at every layer.