According to a 2017 survey from Pew Research Center, Americans are more concerned than excited about emerging automation technologies. Many people don’t look forward to a world in which machines do all of the thinking and work.
Of all the technologies considered in the survey, the one people want least is hiring automation technology. In fact, only 22 percent of respondents reported feeling enthusiastic about the development of hiring algorithms, and 76 percent of respondents said they would not want to apply for a job that used a computer program to select applicants, due mostly to beliefs that “computers can’t capture everything about an applicant” and algorithms are “too impersonal.” This distrust of hiring automation is particularly striking when compared to attitudes toward things like “a future where robots and computers can do many human jobs” (33 percent of respondents were enthusiastic about this) or “the development of robot caregivers for older adults” (44 percent were enthusiastic).
It seems people are more comfortable with the idea of a robot taking care of their grandparents than having a computer interview them for a job. Clearly, there is a personal element to the hiring process that people perceive to be important.
As one survey respondent elaborated, “A lot depends on the sophistication of the program, but I do believe that hiring people requires a fair amount of judgment and intuition that is not well automated. I feel that I have a lot of skills that would be hard to quantify and require that kind of in-depth thinking and judgment.”
This is not an unreasonable evaluation. Machine learning and algorithms operate on a set of mathematical rules that are not designed to take “intuition” into account. They are, by definition, “cold, inhuman, calculating machines.” What is interesting is that people would rather be judged by human intuition than mathematical formulas, even though there are well-documented problems with hiring managers relying on their intuitions during the hiring process. Namely, that humans have an inherent bias in favor of people who are similar to them.
Psychological research suggests that we favor people who look, think, and/or act like us over people who don’t — a phenomenon known as the “similar-to-me effect.” For example, research has shown that employers are more likely to hire a candidate if the candidate is competent and more culturally similar, and that both black and white raters are willing to give higher performance ratings to employees who share their same race. An increasing emphasis on culture fit as a hiring priority in recent years has also garnered much attention from experts who believe this criteria identifies a fit between the interviewer and interviewee, rather than a fit between the interviewee and the organization.
A major advantage of using technology is the ability to detect these types of bias. However, the strong aversion people feel toward hiring algorithms and workplace automation suggests there is a level of misunderstanding and mistrust regarding this technology that business leaders must address.
Here are a few methods of doing so:
