Job seekers are using AI to hide their age from recruiters

Artificial intelligence is changing recruitment from both sides of the interview table. Employers are using AI-assisted technology to process applications and identify candidates, while job seekers are increasingly employing the same technology to reshape how their experience appears to prospective employers.

New survey data supplied to Digital Journal by Use.AI suggests one consequence is the emergence of the AI-assisted “de-aged” CV. Use.AI surveyed 4,263 of its users in the US, UK, Canada and European Union who had applied for a job during the previous 12 months. According to the company, 42 percent said they had edited their CV or LinkedIn profile to make themselves appear younger or less senior. This issue matters because it suggests age discrimination, or simply the expectation that it will occur, could be affecting the information recruiters receive before either a person or an algorithm has evaluated an application.

Among respondents who said they had altered their professional profiles, 72 percent had used AI to help make the changes. More strikingly, the principal concern was not necessarily the recruitment algorithm. Use.AI reports that 56 percent of those making changes believed human recruiters would filter them out because of indications of age or seniority. By comparison, 38 percent expressed the equivalent concern about AI screening.

This creates an interesting twist in the debate surrounding recruitment technology. Attention has understandably focused on the possibility of algorithms reproducing or amplifying human biases. Yet these findings indicate that some applicants are using algorithms to protect themselves against biases they expect to encounter from people. The strategy may also appear to be working from an applicant’s perspective. Of those who changed their CV or LinkedIn profile, 43 percent reported receiving more invitations to interview afterwards, compared with 10 percent who received fewer.

These figures are self-reported and do not demonstrate that removing age or seniority indicators caused the improvement. Other changes made to an application, labour-market conditions and the types or number of jobs applied for could influence interview rates. Nevertheless, the perceived benefit could encourage the practice to spread. AI-assisted recruitment has already become part of a much larger transformation of hiring. Digital Journal has previously examined AI-assisted applicant-tracking and candidate-ranking systems, including the pressure created as recruiters confront large numbers of increasingly AI-polished applications.

Older women appear particularly concerned

The Use.AI results also point towards an important gender difference. Among respondents aged 45 and over, 53 percent of women said they had altered their professional information to look younger or less senior. This compares with 35 percent of men within the same age group.

Across the entire sample, meanwhile, 28 percent thought AI screening treated applicants of their age less fairly than human recruiters would. The apparent contradiction is revealing. Some candidates are concerned that AI systems discriminate against people of their age, yet applicants who actively modify their profiles appear even more worried about human recruiters making judgements based on the same characteristic. This does not demonstrate that either people or algorithms actually discriminated against the respondents. What it does reveal is a perception problem with potentially tangible consequences.

If an experienced employee believes that 25 or 30 years in an industry will count against them, they have an incentive to truncate employment histories, remove graduation dates or soften language that signals seniority. Generative AI lowers the practical barrier to rewriting that professional story.

When experience becomes a disadvantage

For employers, this creates its own problem. Recruiters traditionally use a CV to establish what someone has done and how their career has developed. If candidates start deliberately removing legitimate experience because they fear it will be interpreted negatively, recruiters receive a less complete representation of the available talent pool. This becomes particularly significant for positions where accumulated professional knowledge matters. Someone removing evidence of seniority may also inadvertently conceal the depth of expertise that differentiates them from other candidates.

The consequences could extend beyond an individual application. If large numbers of people optimise CVs around what they think screening systems and recruiters want to see, recruitment risks becoming a feedback loop. Candidates progressively standardise their applications while employers receive less distinctive information with which to differentiate between them. There is already evidence of a related technological pressure. Previous Digital Journal coverage of AI and recruitment noted that high application volumes combined with AI assistance can produce candidate pools in which résumés increasingly resemble one another.

A separate examination of AI hiring systems has highlighted the effort by some technology companies to shift assessment towards demonstrated skills and problem-solving rather than relying predominantly upon conventional résumé signals. That direction could become increasingly important if candidates no longer regard their complete career history as an asset.

A new AI recruitment paradox

There is a broader paradox here. AI recruitment tools are often promoted on the basis that technology can make large candidate pools easier to manage. At the same time, readily accessible generative AI gives applicants increasingly sophisticated tools for modifying the information entering those systems.

This means an employer’s recruitment technology is not examining an untouched record of someone’s professional life. Increasingly, it may be assessing a document already optimised by another AI. The Use.AI findings do not establish whether removing age indicators objectively improves a person’s prospects, nor do they determine whether human or AI recruiters are more susceptible to age bias. The survey instead exposes what applicants believe they need to do to compete. And perception can shape the labour market even when the underlying assumption remains untested. For organisations, the response should therefore encompass more than auditing algorithms. Employers also need to consider whether job advertisements, selection criteria and recruiter behaviour inadvertently communicate that extensive experience is undesirable. Previous reporting has noted the potential for AI hiring technology to move evaluation away from traditional proxies and towards capabilities, although the outcome depends on the design and oversight of the system.

For applicants, meanwhile, AI has provided an accessible means of repositioning a career. What once required a professional CV writer can increasingly be undertaken through a conversational AI interface. The risk is that the optimisation process removes precisely what an organisation ought to value. If experienced candidates conclude that decades of knowledge need to be disguised simply to reach an interview, the recruitment problem is no longer only about whether an algorithm is fair. It is also about whether the hiring process has persuaded people that experience itself is something they need to hide.