To choose the ideal candidates, several firms have begun to depend on computer-administered exams rather than human interviewers. According to new research from Harvard Business School Assistant Professor Danielle Li and colleagues, we may have to score one for the machine in this situation.
In the 1950s and 1960s, job testing was popular as a means of screening through large candidate pools. Testing was phased out in favor of personal interviews when researchers questioned its effectiveness. Machine testing has resurfaced in smart new forms as a result of the growth of big data.
The main question is that what should be done with the information gathered with the help of AI and machine learning. The growth of AI has been thrown out of the pool when researchers have questioned its effectiveness, but due to growth in the field of Ai and big data, testing can be introduced, as the technology is improving.
Testing companies use a variety of different methods for choosing the ideal candidate; Personality test, skills assessment, maths, and logical problems. All the information gathered would be fed to machine learning algorithms which will determine which candidate is perfect for a particular scenario or condition.
Hold On! The dilemma is how much this information should be valued in comparison to the more personal observations gained during job interviews.
Is it Effective:
AI interview software has been utilized by major organizations such as Hilton and Unilever, and an estimated 83 percent of US companies use some sort of AI in their HR operations.
According to Prasanna Tambe, an Associate Professor of Operations, Information, and Decisions at the University of Pennsylvania’s Wharton School of Business, “HR decision-making has always been based on recruiters’ prior experience and their intuition about a candidate.” “Because technology has made it easier than ever to apply for employment, recruiters are faced with ever-increasing stacks of candidates to assess, which takes time. Recruiters are interested in tools that can help them go through even more applicants and make well-informed decisions.”
According to a study, 73 percent of candidates had no idea they were engaging with a chatbot when they approached organizations to ask questions, and in most situations, candidates have no way of knowing AI was used to screen their application.
In an even more detailed apples-to-apples comparison, the researchers made use of two candidates for a job at the same time — one yellow and one green. When the yellow worker was employed as an exception and later on the green worker was hired, they put them head-to-head to see which one stayed longer. They discovered that the green workers who were passed over performed better, staying an average of 8% longer, meaning that the management would have been better off hiring the green worker in the first place rather than making the exception.
Why are humans so fallible?
It’s tough to predict from the data exactly what recruiting errors managers are making, but they’re presumably not purposely hiring people who aren’t up to the task. Managers are most likely putting up their best efforts to hire the ideal applicants. However, when compared to an algorithm that has access to far more data on worker outcomes and has been trained to spot these patterns, they are not as good at predicting that.”
Many things we often think of as connected with performance are not, according to studies in different situations; for example, school teachers with master’s degrees in education perform no better in their professions than those who do not. “ As a result, it’s likely that hiring managers are putting too much emphasis on things that look nice on paper but don’t matter much in reality.
Is this the end of the conflict between man and machine? Certainly not!
When it comes to hiring decision, there can be many aspects which should consider while deciding which is the ideal candidate while hiring the ideal candidate. However, when it comes to the types of hiring decisions examined in the study, it may be time to admit that there are some things that machines can do better than people. It’s easy for people to believe that they’re acquiring useful information from interviews, and their judgment is valuable, but is it more important than quantitative information?