Feb 01, 2016

Algorithmic Hiring

Countless studies have proven that people are biased when it comes to decision-making. In the world of talent acquisition, this inherent bias can lead to inadvertent discrimination, or workplace homogeneity, due to the tendency of interviewers hiring candidates who remind them of themselves.

Workplace homogeneity has received a bad reputation in the media recently, especially among technology companies. Google’s recent diversity report, for example, reported that only 2 percent of its staff was black, and only 3 percent was Hispanic. And at Twitter, just 10 percent of technical employees are women; at Facebook and Yahoo, about 15 percent of tech employees are women. The tech sector is trying to correct this imbalance implementing practices and business rules that aim to recruit and onboard skilled workers from diverse backgrounds. For instance, Facebook recently announced that they were going to try using the NFL’s “Rooney Rule” to expand its staff beyond just white and Asian men.

What is Algorithmic Hiring?

 Another proposed solution that is also making waves is using algorithmic hiring to remove biases. Algorithmic hiring is defined as using an algorithm to systematically analyze data and make hiring decisions. Companies, for example, can administer personality tests to candidates during the screening process, and then use data analysis to determine ideal hires. The algorithm can use not only what the company is looking for, but also common variables such as data from personality tests to predict candidate fit.

 Over the past few years, algorithmic hiring has been steadily growing more popular. Google, for instance, uses an algorithm to quickly add staff, using an elaborate survey to identify candidates who will be fit for the company’s culture. And studies have proven the merits of an algorithmic approach to making hiring decisions. An analysis by the Harvard Business Review found that a simple equation outperforms human decisions by at least 25 percent. This holds true in any scenario with any number of candidates, or any type of position.

Examples of Algorithmic Hiring at Use

 Direct marketing company Harte-Hanks uses algorithmic hiring to select the best candidates for call center positions. The company has found that using an algorithm had great results. Worker who were algorithmically hired had a 35 percent lower 30-day attrition rate, reported 29 percent fewer hours of missed work in the first six months, and handled their duties 15 percent more quickly than those hired through the company’s traditional recruiting methods.

 Various startups are finding ways to automate hiring. Even established recruiting firms like Korn Ferry are incorporating algorithms into their processes. Some types of algorithms use machine learning and language analysis to analyze job postings to uncover phrases that indicate gender bias.

The Skeptics

Some people say that while an algorithm can do a better job of removing biases, it’s unable to understand people in the same way as a human would. Some surveys suggest that when it comes to assessing individuals, 85 to 97 percent of professionals rely on some degree of intuition. Many managers believe that they can make the best decision by looking at an applicant’s portfolio and meeting them face-to-face, and that no algorithm can substitute for that.

“I look for passion and hustle, and there’s no data algorithm that could ever get to the bottom of that. It’s an intuition, gut feel, chemistry.” ~Amish Shah, Founder and Chief Executive of Millennium Search

“Every company vets its own way, by schools or companies on resumes. It can be predictive, but the problem is it is biased. They’re dismissing tons and tons of qualified people.” ~Sheeroy Desai, Co-founder and Chief Executive of Gild