Popular Culture Review 30.1
absenteeism , or that a love of Manga will make an employee a better coder . 47 There is no guarantee that the ideal employee is not amongst the large percentage of resumes discarded by the system . Algorithms are , at their basic level , opinions , biases , and prejudices embedded in code . 48 While they can mimic human decision making if the decision making criteria are fundamentally flaws , those flaws will be present in the final output . The old programmer truism remains as true today as ever . Computers make very fast , very accurate mistakes .
While algorithmic hiring can be a powerful tool for employers , it contains the very real risks of inadvertently or intentionally discriminating against those protected by Title VII . Data analysis can also reveal existing bias inside the company ’ s hiring practices . Companies have used machine learning and language analysis to analyze job postings . In doing so , have identified phrases that indicate existing gender bias . 49 Language-like “‘ top-tier ,’ ‘ aggressive ,’ and sports or military analogies like ‘ mission critical ’ decrease the proportion of women who apply for a job . Language-like ‘ partnerships ’ and ‘ passion for learning ’ attract more women .” 50
By hiring employees with similar qualities and skillsets to existing employees , employers enjoy a better chance of hiring the right person for the role . On the surface , algorithmic hiring can assist companies with translating stacks of resumes into hiring qualified applicants . This can significantly reduce both the time and costs of identifying which employees to hire . A poor hiring decision can result in the sunk costs of hiring and training an employee who may later be fired due to being unable to perform their duties . Companies that have used algorithmic hiring have reported positive results such as a 38 percent reduction in employee turnover . 51
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