Automated Recruiting Systems Are Dismissing Millions Of Talented Applicants, Says Report


Jack Dunhill

Social Media Coordinator and Staff Writer

clockSep 9 2021, 14:54 UTC

AI is deciding your job fate  and it isn't very good at it. Image Credit: pathdoc/

If you’ve applied for a job, particularly at a large company, in the last few years, you’ll likely be aware of the terrible state of recruiting. Resumes seem to disappear into limbo for months on end, ambiguous job descriptions, and companies that aren’t even currently recruiting but still create listings are just the start, all before being rejected for an unknown reason. 

Part of the cause of this almost impossible labor landscape is automated recruitment software. These algorithms sift through hundreds or even thousands of applications and shortlist those they deem worthy, often passing up perfectly viable candidates due to small technicalities. 


Now, a new report by Harvard Business School has highlighted just how much damage they are doing to the labor system, and how they are contributing to a “broken” hiring system. 

“Companies are increasingly desperate for workers. As they continue to struggle to find people with the skills they need, their competitiveness and growth prospects are put at risk,” reads the opening of the report.  

“At the same time, an enormous and growing group of people are unemployed or underemployed, eager to get a job or increase their working hours. However, they remain effectively “hidden” from most businesses that would benefit from hiring them by the very processes those companies use to find talent.” 

The report involved a large survey of 8,000 “hidden workers” and more than 2,250 executives across the US, UK, and Germany. Hidden workers are considered as people that wish to work, but are unable to do so due to the job market or personal reasons. Through the survey and taking an in-depth look at the employment scene, the researchers discovered two crucial things: firstly, workers were struggling to find work before the pandemic hit, hinting that the problem is not solely due to lockdowns; and that the AI systems firms are using are preventing millions of talented individuals from ever seeing an interview.  


These systems in question are either Applicant Tracking Systems (ATS), Recruiting Managing Systems (RMS), or both used in conjunction. In middle- and high-skilled jobs, over 90 percent of employers in the survey stated they used RMS to filter or rank applicants.  

Scouring through resumes to identify key skills and qualifications, these systems allow large firms to streamline their process and find suited individuals fast, but they come with a host of issues. The report does not hone in on a specific reason why these systems can miss viable candidates, but they suggest that systems often sort applications into "good" and "bad" categories over simple discrepancies. Ludicrously, they may even filter candidates out for not having a highly specialist skill implicitly stated on the resume, or if they answered honestly to a recruitment form, instead of ticking "expert" on every skill requirement. Even the employers agree – these systems are missing talent.  

“A large majority (88%) of employers agree, telling us that qualified high-skills candidates are vetted out of the process because they do not match the exact criteria established by the job description,” states the report. 

To remedy the loss of talent through electronic systems, Harvard has some recommendations. 


The companies must first re-evaluate their job descriptions. Instead of constantly adding skills that are "necessary" to get an interview, refresh the descriptions with a small number of “must-have” skills that allow worthy candidates opportunities. They should also change the recruitment AI, by converting negative filters, that de-rank applicants based on missing skills or employment gaps, into filters that rank applicants higher for those that fill the role the best. This way, the best candidate would be found out of the pool, instead of instantly disregarding candidates for often menial issues.  


  • tag
  • algorithm,

  • AI,

  • recruitment