How to beat algorithms in job hunting?

Despite being a good fit in a company, you could be disqualified in the selection process. How to beat algorithms in job hunting?

A rising critic of the use of AI for different tasks is that its algorithms could discriminate against persons that already are discriminated against. Trained with human bias, the algorithms are leaving out candidates that potentially could be a good fit for a job post. Brazilian media Revista Época Negócios shared some tips to beat the algorithm in job hunting.

In some cases, it’s not the skills or experience of candidates that lead them to get a job in a dispute with hundreds of other candidates. Knowing how the algorithms work can help a candidate to not be disqualified in the process.

Hiring processes are being outsourced to AI

Companies have to deal with complex processes and often delegate that work to artificial intelligence systems that analyze resumes. That’s how it’s decided whether you’re eliminated or whether you continue in the process.

These are predictive hiring programs that automatically apply certain predetermined criteria, according to the characteristics of a vacancy.

These tools, known as ATS (Applicant Tracking System, or Candidate Tracking System), work on a base of keywords that each company defines according to the profile search, in addition to using other mechanisms to eliminate candidates.

There are different types of ATS, but in general, the operation has a very similar mechanism.

These systems allow companies to save time and money. But they can also be a disadvantage when the algorithm, because of its limitations, decides to eliminate good candidates or when it develops biases based on repeating patterns.

How to beat the algorithm in job hunting?

Despite the bias that the algorithm works, candidates can increase their opportunities following these bits of advice:

  1. Use keywords: To use keywords, the first step is to compare your resume with the job specifics description. The algorithms look for keywords related to categories such as skills, experience, performance, and education and adjust these results to the requirements of the job. But the company can ask that more specific filters be included. To use keywords, the first step is to compare your resume with the job specifics description. The candidate can then include in his/her resume terms and expressions used to describe the function, making only adaptations to his/her own profile. To use keywords, the first step is to compare your resume with the job specifics description. 
  2. Include achievements as quantifiable results: On this point, it is important to be specific. For example, a person from the tech field writes down the names of the software he or she has mastered instead of saying “data analysis experience” without going into detail. Experts also recommend showing career achievements through concrete examples rather than citing a list of assignments from past jobs. However, not all companies measure job performance in the same way. For that reason, it’s recommended to investigate what the measure of success is for the company that a job seeker is applying to.
  3. Use a simple format: Many people are not selected in a process for something very simple: the readability of their resume. In order for the algorithm not to eliminate you, it is essential that your CV format is simple and “decipherable” by the system. On some ATS software, resumes in PDF format do not work. Avoid using any complicated formats. The simpler the better. That is, it is recommended to use Word. Also, try not to assemble the resume into two columns. Use the standard format for the program to read the “race” page. Always place the places you’ve worked in reverse chronological order—that is, most recent first.

Don’t be disqualified, don’t disqualify

Several of these suggestions apply to a resume that will not be reviewed by an algorithm. So, it’s a good idea to apply them in your standard review.

However, if you are an employer or developer for Human Resources, it’s a good time to think about all the good fits that a company loses due to a biased program.