Three Ways Machine Learning Is Improving The Hiring Process

Technology’s advance into all industries and jobs tends to send ripples of worry with each evolution. It started with computers and continues with artificial intelligence, machine learning, IoT, big data and automation. There are conflicting views on how new technology will impact the future of jobs. But it’s becoming clear that humans will need to work with technology to be successful — especially as it relates to the hiring process.

There’s a great example of this explained by Luke Beseda and Cat Surane, talent partners for Lightspeed Ventures. On a recent Talk Talent To Me podcast episode, they spoke with the talent team at Hired, where I work, about why it’s critical to understand why a candidate is pursuing a given job. They concluded that machines can’t properly manage the qualitative aspect of hiring. For example, machines can’t tell if a candidate is seeking higher compensation or leveraging a job offer to negotiate new terms with their current employer. Humans can.

However, machines are better at making processes more efficient. For example, machine learning brings value by processing job applications faster than humans — which can reduce the amount of time it takes to recruit and hire a new employee.

With that in mind, here are three ways machine learning is improving the hiring process:

Most HR professionals today use recruitment platforms to find potential employees through a search-based system where they can narrow down a list of candidates based on factors like skill, industry, experience and location. But with machine learning capabilities, hiring managers don’t have to manually dig through applications from hundreds of candidates to find the best fit. Instead, they can rely on networking and job sites to leverage machine learning and offer intelligent recommendations on the candidates who can fill a given role. This enables a more efficient hiring process for both job seekers and recruiters.

Machine learning can help level the playing field in hiring. It can be employed to provide equal exposure to opportunities, regardless of a candidate’s pedigree or background. Algorithms should focus on skill-based data, not on the universities where a candidate has studied, the companies where they have worked, or their ethnicity or gender.