If there was ever any doubt as to the importance of successful recruitment, we need only look at the current labor shortage – and the high quit rate in the workforce – that is plaguing industries across the United States. For instance, the US Chamber of Commerce estimates that there are currently over 10 million job openings in the U.S. with roughly 6 million job seekers to fill these positions.
In an effort to adapt to the changing expectations of workers and job seekers and to minimize the negative impact of trends such as The Great Resignation and quiet quitting, recruitment managers are increasingly turning to technology (more specifically machine learning) to help them improve the recruitment process and increase their chances of finding qualified candidates.
As humans, we make associations and inferences based on emotions and individual experiences. For example, a recruiter might come across the resume of a qualified applicant and see a former place of employment or a location that reminds the recruiter of their own experience–either positive or negative. This association may then influence the recruiter’s judgment of the candidate. If this influence is recognized, it is known as a conscious bias. In many instances, the recruiter is unaware of the influence and is thus betrayed by an unconscious bias.