{"id":811358,"date":"2026-07-19T03:10:28","date_gmt":"2026-07-19T03:10:28","guid":{"rendered":"https:\/\/www.newsbeep.com\/ca\/811358\/"},"modified":"2026-07-19T03:10:28","modified_gmt":"2026-07-19T03:10:28","slug":"opinion-the-hunt-for-a-job-has-never-been-worse-these-applicants-are-fighting-back","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ca\/811358\/","title":{"rendered":"Opinion | The Hunt for a Job Has Never Been Worse. These Applicants Are Fighting Back."},"content":{"rendered":"

I recently wrote about the purgatory job market<\/a> of 2026, in which potential employees are largely evaluated by automated systems, engage in chatbot interviews and, even then, often get no feedback about their applications. Multiple job seekers described this experience to me as a \u201cdispiriting,\u201d \u201cdehumanizing,\u201d \u201cdystopian\u201d netherworld.<\/p>\n

After that story ran, I got several emails from start-up founders who told me that their new tech product was going to help fix the broken job search. I spoke to a few of them, all of whom identified a real \u201cpain point\u201d on the employer side: Corporations receive way too many r\u00e9sum\u00e9s to evaluate, yet they are still having trouble finding genuinely skilled and appropriate employees. What\u2019s more, some job seekers are using A.I. to misrepresent their skills and sometimes even who they are.<\/p>\n

These start-ups claimed that their artificial-intelligence-infused products \u2014 whether an app offering a unique set of cognitive games and tests or a new way to crunch data and evaluate promising and unique candidates \u2014 would streamline and validate what machine learning has complicated and obscured. Their products involved collecting a lot of information on potential employees through scraping data that is already available on the open internet or by monitoring every keystroke and movement a potential employee makes in a proprietary app.<\/p>\n

I remain skeptical that adding yet another layer of artificial intelligence and surveillance into a demented process is the best solution. As employers continue to use A.I. to rationalize the process of identifying strong applicants, applicants have begun to use similar tools to game the systems evaluating them. The vicious cycle thus begins: Employers buy or build new tools, which job seekers cotton on to, and on and on. It becomes a futile \u201cSpy vs. Spy<\/a>\u201d showdown instead of a useful way to meet the ostensible goal of giving qualified people jobs.<\/p>\n

According to a 2026 report from the Manpower Group, one of the largest staffing organizations<\/a> in the world, \u201c72 percent of employers said they are struggling to find the skilled talent they need.\u201d Ifedapo Adeleye, the faculty director for the human resources management master\u2019s program at Georgetown University, told me that while A.I. might be making some tasks more efficient for human resources workers, the key outcomes \u2014 finding the right people and reducing the time it takes to hire those people \u2014 aren\u2019t happening any faster and the entire process is no more precise. There\u2019s also not good evidence that companies are retaining the employees they do hire. Gallup polling suggests that 52 percent of American workers<\/a> are watching for or actively seeking a new job, the highest percentage since such polling began in 2014.<\/p>\n

This is a problem that seems as if it could be solved only through a return to some analog processes and better regulation at the federal level that forces employers to make their current methods of evaluation more transparent and fair.<\/p>\n

The failed promise of automated hiring<\/p>\n

Employers have been using machine learning to evaluate potential employees for over a decade, long before most of us were thinking about artificial intelligence, said Ifeoma Ajunwa, a law professor at Emory University and the author of \u201cThe Quantified Worker: Law and Technology in the Modern Workplace.\u201d<\/p>\n

Ajunwa became aware of the ubiquity of automated hiring systems in the 2010s, when she was interviewing formerly incarcerated people. The interviewees had paid their debt to society and were taking classes to build job skills. But the automated hiring systems appeared to reject them outright either because of gaps in employment or because they honestly answered a question about their criminal history.<\/p>\n

A wave of start-ups in the mid-2010s claimed that using algorithms in hiring<\/a> would make the process less biased and more efficient. By 2011<\/a>, over 75 percent of job seekers looked online for jobs, and companies were flooded with too many applications to process, so they did need some way to begin vetting. (The volume of applications has become even more unmanageable<\/a> since ChatGPT became available in 2022; per The Economist, \u201cPaid services like LazyApply and aiApply let candidates submit applications while they sleep, tailoring r\u00e9sum\u00e9s and cover letters to a tee.\u201d) But machine learning often reinforced existing biases<\/a> and, for many human resources professionals, added new complications like fake applicants and more cheating on skills assessments.<\/p>\n

In 2018, Reuters broke the news that Amazon had built a recruiting tool that discriminated against female applicants because its models were<\/a> \u201ctrained to vet applicants by observing patterns in resumes submitted to the company over a 10-year period\u201d and \u201cmost came from men, a reflection of male dominance across the tech industry.\u201d Amazon stopped using that tool, but racial and gender biases<\/a> have been revealed through academic analysis<\/a> and individual audits across many different industries.<\/p>\n

A darkly funny example from Ajunwa\u2019s book is what she calls \u201cthe story of Jareds.\u201d A corporation had a lawyer audit its automated hiring system before implementing it. The lawyer asked the system, \u201cWhat two factors would your system consider as the most important for choosing a candidate?\u201d The system responded, \u201c(1) the candidate is named Jared and (2) the candidate played high school lacrosse.\u201d The bot, of course, knows only the \u201cgood fit\u201d r\u00e9sum\u00e9s on which it has been trained.<\/p>\n

This state of affairs is not working for human resources managers, who tend to get into their field because they actually like people, and surely don\u2019t want to hire an army of Jareds. And there isn\u2019t good evidence yet that automating hiring has resulted in a better process or better results.<\/p>\n

Mercy for job applicants<\/p>\n

The most important remedy for job applicants is likely fixing the laws around automated hiring at the federal level so that there is much more transparency about the use of A.I. and your personal data.<\/p>\n

New York City\u2019s laws could be a model for such legislation, my newsroom colleague Steve Lohr pointed out in 2023<\/a>. \u201cThe city\u2019s law requires companies using A.I. software in hiring to notify candidates that an automated system is being used. It also requires companies to have independent auditors check the technology annually for bias,\u201d he wrote.<\/p>\n

Ajunwa also thinks that the Fair Credit Reporting Act should apply to all A.I. companies that collect data on applicants and then give them a score. Applicants should be able to access that score and \u201ccheck it for inaccuracies, and also demand a correction from whoever holds that file,\u201d she said. This is the argument behind a class-action lawsuit filed in January<\/a> against Eightfold AI, a company that uses a data set that includes the profiles of more than 1 billion people. The plaintiffs want transparency into how they were scored and what information was used to assess them.<\/p>\n

None of this will solve the fact that we are in a legitimately tough market for job seekers. There are more applicants for open roles<\/a> than there were<\/a> a few years ago<\/a>, and many employers do not seem to be looking for diamonds in the rough<\/a>; human resources experts are telling me that corporations are ramping up their expectations and requirements because they\u2019re in a buyer\u2019s market. Forcing employers to offer some kind of transparency and even feedback to potential employees would help correct the power imbalance and make the process of looking for a job feel at least slightly more human.<\/p>\n

One solution for employers is to go back to some of the imperfect analog methods of recruiting. In an essay in The Atlantic about the heinous job market, Annie Lowrey observes that<\/a> \u201csome firms are resorting to old-fashioned methods: referrals, alumni networks, local job boards, headhunters.\u201d I would love to hear about what happens if a company experiments by requiring all potential employees to mail in a r\u00e9sum\u00e9. (If any hiring managers are doing this and want to tell me about the experience, email me here<\/a>.)<\/p>\n

\"\"\/<\/p>\n

Jessica Grose<\/p>\n

Opinion writer<\/p>\n

Even if you’re not looking for a job right now, machine learning will be assessing you at some point. When I spoke with Ifeoma Ajunwa, author of \u201cThe Quantified Worker: Law and Technology in the Modern Workplace,\u201d for this piece, she predicted that as different segments of the population seeking employment are systematically excluded by A.I. systems, it will be just a matter of time before anyone looking for a job is affected.<\/p>\n

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