Predatory AI Recruitment (& How To Beat It)

Join us as we talk about…

In this week’s episode…

• You’ll learn about the use of AI in recruitment to help filter out applicants
• Next, you’ll learn how AI can be discriminatory while filtering out applicants
• Finally, you’ll learn how why it is important to craft strong resumes in order to get through the applicant tracking systems.

“Isaiah, I’ve uploaded hundreds of resumes and have not had a single interview. In some cases, I’ve been rejected immediately – as in the same day. How is this possible? How did they determine I was a bad fit already.”

Unfortunately, I hear this all the time now. I also hear this – “Isaiah, I applied to this job last week but they just reposted the same job. What should I do? Apply again?”

Welcome to the world of predatory AI. Let’s back up so you can understand the scale of AI use first. The use of AI in recruitment has become extremely commonplace, with 99% of Fortune 500 companies in the U.S. relying on applicant tracking systems (ATS) to filter through a large number of applications.

Did you catch that? 99%.

The only companies who don’t do this are those with very small numbers of employees, like 10 or less.

While these systems are designed to increase efficiency and manage the overwhelming number of candidates, they also have a dark side. Studies have shown that AI can perpetuate discrimination, as it is often trained on biased data from previous hiring practices.

For example, Amazon’s hiring software, developed over four years, was found to score one gender differently than another when all else was equal.

Another screening tool used by big named tech companies was found to rank candidates with certain names and backgrounds more favorably. This raises concerns about the objectivity and fairness of AI-powered hiring tools.

On top of this, AI systems have limitations and can screen out qualified candidates due to factors such as gaps in their resumes or lengthy job postings.

These systems do not possess human-like reasoning abilities and can misinterpret information. For example, a journalist applying for a job was ranked low by an ATS screener because it did not recognize their international experience as meeting the requirement.

Worse, many of these AI systems are being used to harvest as much information as possible about the job market and job candidates as possible without offering any real job opportunities.

This has become known as ghost jobbing or ghost job listings. Companies will use their AI to refresh jobs every week or similar so that job seekers keep applying and providing free data and free resumes for some future use (or no use at all).

In fact, AI has become so predatory in this way that some regulatory agencies are starting to look into it. I wouldn’t expect change any time soon though. And even if posting ghost jobs becomes illegal in some way, companies will just stop posting fake jobs; it’s not like this will somehow create more real jobs.

In conclusion, while AI recruitment systems have their advantages in managing large volumes of applicants, they also raise concerns about bias and limitations in accurately assessing qualifications. Job seekers should be aware of the limitations of these systems and focus on creating strong resumes that align with job requirements, and reaching out to real people behind these AI systems to ask if the job really exists and if they’re looking to fill it in the near future.