Applicant tracking systems
An applicant tracking system (ATS) is software that supports the recruitment and hiring of job candidates. It simplifies the recruitment process and automates the processing of resumes. It is estimated that around 80 percent of resumes go through ATS as the first step of the recruitment process, lowering the workload of recruiters and human resources managers as it reduces the need for having to process resumes manually.
Traditionally, human resources departments relied on keeping things on paper, keeping resumes in filing cabinets with limited ways to store and track applications in an organized way. This made the recruiting process time consuming and costly. There was also inconsistency in hiring processes as different locations and hiring managers would have different standards with limited ways to coordinate, which could lead to lawsuits against the company. 
The first-ever ATS which aimed to address some of these concerns was Resumix, which was introduced in 1988. However, it took some time for ATS to gain a foothold, as in 1995 fewer than 300 companies used ATS to store, organize and search resumes.  Today, ATS is commonplace, especially in large corporations that receive a high volume of applicants online. An estimated 99% of Fortune 500 companies use some form of ATS, compared to 40% usage across all employers. 
In recent years, some ATS has started to integrate artificial intelligence algorithms and natural language processing technology. This improves the capability for ATS to parse resumes correctly and identify synonyms for what the employer is looking for. Some ATS also specialize in browsing social profiles on sites such as LinkedIn, performing tasks like identifying potential candidates matching the employer's criteria who are likely to want to leave their current job. 
ATS acts as an improved tool for resume parsing and tracking different applicants, giving the ability to automate the recruitment process. As there are many ATS services offered and developed by different companies through the software as a service (SaaS) model, there is a wide range of what kind of functionality is offered by ATS, which may often include interview scheduling, delegating responsibilities between HR personnel and keeping track of what stage of the hiring process an applicant is at. Some companies developing ATS expand the scope of ATS to allow for post-hire tracking, allowing for a detailed record of an employee to be stored in the company's database starting from the initial hiring process and including performance appraisals. 
ATS can help to make the recruitment process more simple and transparent. Using ATS allows for companies to improve the consistency of hiring at multiple locations, which can help the company avoid legal issues surrounding the hiring process. Additionally, ATS can help produce metrics to enable HR leaders to identify the most fruitful sources for job candidates as all data will be available for further analysis. Stored candidate data produced by ATS can also be reused by recruiters when they are looking to fill up new job positions, as recruiters are able to use ATS to set up search parameters instead of having to manually look through resumes that may fit the job criteria. 
In a typical online job application process involving ATS, a candidate will upload their resume, with some companies requiring the candidate to answer additional questions and fill out a job application form alongside this. The ATS will then read and parse the candidate's uploaded resume, with information from the resume being put into set fields in the ATS database. Many ATS use clues from the resume such as standard headings to try to interpret what each section of the resume presents (e.g. distinguishing the candidate's education from their previous work experience). 
ATS often use specific keywords to score applicants. When configuring the ATS, a recruiter will often specify set keywords that the ATS will be scanning for when parsing the resume. The ATS will then score applicants based on how many of the keywords were found within the resume, with elements like typos potentially causing a decrease in score. If a resume receives a high score, there is a higher chance that a recruiter will manually look at the resume; conversely, if a resume receives a low score, it is likely to be ignored.
ATS has been known to run into issues while parsing resumes. For example, most ATS resume parsers are unable to interpret images correctly, so elements like charts and infographics will be disregarded by the ATS and may even cause the resume to process incorrectly, which would likely result in the resume not being seen by a human due to poor scoring from the ATS. Because of this, it is often advised that job applicants stick to simplistic plain text formatting avoiding graphics and extraneous elements such as headers and footers when writing their resumes. It is also a common strategy for job-seekers to try to "stuff" as many keywords into the resume as possible so that ATS gives positive feedback and thus is able to have their resume forwarded to a human recruiter. 
Resume "black hole"
Partially owing to the ubiquity of ATS in large corporations, it is estimated that 75% of submitted resumes are never read by a real person. This likely means that many qualified applicants never have their applications read by an actual recruiter due to having their resume filtered out by the ATS, which is unlikely to consider an applicant and their previous experiences holistically and instead only considers which keywords have been included within the resume. This phenomenon has been dubbed as a resume "black hole" as an applicant sends out an application and is likely to never have their resume reviewed by a recruiter, with the application being seen as similar to disappearing forever in a "black hole".
The use of ATS can be seen as giving an unfair advantage to job applicants who know how to exploit ATS to their advantage. If a job applicant knows the specific keywords the ATS is looking for and adds all of these keywords to their resume, this causes them to gain a massive advantage over other job applicants who may potentially be more qualified. In the case where a graphical resume is used and the ATS is unable to parse the resume correctly, this causes job applicants to be unfairly penalized when they may have been expecting a human to review their resume and thus attempted to make their resume look more aesthetically pleasing. This issue is especially concerning for people in creative industries such as graphic design, where many job applicants want to use their resume as an opportunity to showcase their ability.
In 2015, it was revealed that Amazon's ATS, which included machine learning algorithms to find the best candidates, showed bias against women. Technical roles are generally male-dominated. Amazon's system taught itself that male candidates were preferable over time as it used existing Amazon employees as its training dataset for what would be desirable in a job candidate. Thus, it penalized resumes that included the word "women's", thus excluding members of women's professional associations and female-only colleges.
After this was discovered, Amazon scrapped the engine that they were using and made changes to their algorithm. However, this raises ethical questions about using ATS in hiring, particularly as AI and machine learning algorithms are becoming increasingly integrated with traditional ATS.
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