The Effects of Big Data During the Hiring Process

Entering the workforce is already hard enough as it is for a lot of people. To make the process even more complicated, Weapons of Math Destructions (WMD) are weeding out groups of people through personality tests. Chapter 6 of “Weapons of Math Destruction,” by Cathy O’Neil, addresses how big corporations are using WMDs to lessen the workload during the hiring process using WMDs. One such example is the software developed by Kronos. We will refer to this software as a WMD due to the harm it causes to our society. One of the characteristics that this WMD possesses is that it is opaque, like many other WMDs mentioned in the book thus far. It evaluates job candidates through a personality test. Often, candidates are not aware of why they fail to land a job, such as the case of Kyle Behm, a student of Vanderbilt who was unable to land minimum wage jobs due to the WMDs.

The series of questions in the personality test is limited due to the answers available. People have different personalities as well as different perceptions of personalities. Therefore, questions in a personality test should not be limited to only two possible answers that test takers are forced to choose. One example provided in the book was that the word “unique” is often a sign of narcissism in the personality test administered by the WMDs. At the same time, others may correlate uniqueness as a positive trait. For someone competing against other applicants for a specific job position, they would want to be unique and stand out from the rest of the applicants to increase their chances of receiving the job offer. A solution that O’Neil offers for this dilemma is that instead of rejecting applicants, WMDs should offer help instead. This solution addresses the opacity of the WMDs by providing feedback to applicants and informing them of how their results came to be. An important takeaway in this chapter is that big data models should never take a bundle of inputs that are considered untested assumptions, much like the field of pseudoscience, because they often cause more harm than good.