Does Your ATS Need Training?
A Little History Lesson…
There was a time, not too long ago when recruiting was more of an analogue process. Print and radio advertising were the preferred ways to advertise for talent especially for high volume roles. Fast-forward to the mid-1990s when online job posting sites like CareerBuilder and HotJobs were created and digital recruiting was born. The way we recruit and searched for jobs changed forever. Additionally, the digitalization of job search platforms has removed the cost and difficulties that go along with that process.
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The removal of these obstacles “caused the number of applicants per position to explode from 100 applicants per job in 2013 to an estimated 250 in 2018” (Esch & Black, 2019). Job seekers now had a plethora of job postings from various companies at their fingertips. Similarly, recruiters and talent acquisition professionals had an increased number of candidates to choose form. There can now be thousands of applicants for one job posting…seriously!
When I started my recruiting career in 2010 online recruiting search engines like CareerBuilder, Monster.com and Indeed were must haves for recruiters. As a direct result of the growth in popularity of these job sites, companies invested in Applicant Tracking Systems (ATS) like Taleo, Kanexa and Workday. Some companies created their own applicant tracking systems, such as my former employers Amazon and Meta. Recruiters use ATS to search candidates who applied to their job requisitions via the various search engines. These ATS can also search candidates who matched Boolean key word searches whether they applied to the job or not.
How AI is Changing the Recruiting Landscape
In today’s unique workforce where company culture and diversity are among the leading values for candidates and companies alike, recruiting teams are competing for top talent by engaging potential recruits across multiple platforms. Among the challenges for these teams is how to attract and recruit the ideal workforce for their clients especially in high volume recruiting.
There is also the additional component of screening through an almost insurmountable volume of candidate applications. Hence, AI tools are becoming increasingly popular for recruiting teams seeking efficiency and accuracy. Among its many capabilities, AI software includes programs that source, screen and select top talent. However, the use of AI in recruiting has come with some criticism. There have been verified reports of the technology's inability to dismiss bias in the recruiting process.
Amazon for example, scrapped its AI recruiting program in 2015 after its machine learning team discovered that their recruiting engine did not favor women regardless of their qualifications. The computer models erroneously learned to favor male candidates because, most of the preferred candidates in the past had been males, according to a 2018 report from Reuters.
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Facial recognition software in recruiting has also come under scrutiny by civil rights groups and HR professionals. Research has shown that when AI relies on facial recognition it can often misidentify or misread faces of color especially among darker skinned women according to a 2020 report. A prominent watchdog group, The Electronic Privacy Information Center (EPIC) asked the Federal Trade Commission (FTC) to investigate HireVue which uses video on their interview screening platform, based on a 2019 Washington Post report. HireVue analyzes word choice and facial expressions to screen candidates who submit video responses to employers. The request to the FTC claims that the company is not being transparent in its use of algorithmic assessment. HireVue denies the use facial recognition in its software.
Looking Ahead…
AI has made my recruiting projects easier and more organized. I am able to cast a wider net and reach more potential candidates thus accomplishing my "jobs filled" goals at a faster rate. However, recruiters and companies must do their due diligence. Remember, recruiting is not a set it and forget it function. Before implementing AI technology or any programs that make decisions for your business, scrutinize your vendor and ask the hard questions. For example, what is the probability of intrinsic bias being programed into this algorithm in relation to candidate selection? Part of that scrutiny should also include examining the data and reports on potential vendors. Moreover, if bias is detected, take Amazon's example and scrap it! Ensure that the AI program that your company adopts is not working against the important goals of diversity, equity and inclusion.
