Algorithms for hire

Lindsey Zuloaga explains how machine learning from video interviews is disrupting the human resources space, bringing top candidates to the attention of recruiters and drastically reducing the time and energy companies spend finding and assessing potential employees.

Most of us know the job market can be difficult to navigate. Traditional hiring practices are plagued with inefficiencies and bias that prevent hiring managers from leveraging the talent in their applicant pool. Invariably, throughout the hiring funnel, candidates are subject to inconsistent or unfair human judgments, which can vary depending on the evaluator, their mood, time of day, etc. Furthermore, for many large companies, the sheer volume of applications makes it impossible to fairly review each candidate, meaning people get eliminated based on criteria with weak ties to job performance, such as GPA. A popular solution to sifting through a large number of applications is the practice of mining resumes for specific keywords, sometimes even applying machine learning algorithms in attempt to learn the resume makeup of top performers. These evaluations, along with more in-depth personality assessments arguably are not representative of a person as a whole. Lindsey Zuloaga explains how machine learning from video interviews is disrupting the human resources space, bringing top candidates to the attention of recruiters and drastically reducing the time and energy companies spend finding and assessing potential employees. Lindsey explains how HireVue sources a large database of recorded online interviews and uses sophisticated machine learning algorithms to analyze vocabulary, speech patterns, and facial expressions to match job candidates with opportunities. Models are trained using performance metrics for existing employees in a given role. Then new applicants are scored on their potential to succeed in that role, ensuring the best-suited candidates get the attention of recruiters. Lindsey concludes with a discussion of how this technology can be utilized to give equal opportunities by recognizing underrepresented groups and calling attention to reviewer bias based on gender, age, and ethnicity.