Algorithm Beats Humans In Hiring Candidates

by CXOtoday News Desk    Mar 10, 2016

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Algorithmic hiring has been on the rise in recent years. While there can be scope for biases and other error in the human hiring process, it is said that using an algorithm can remove such flaws. Research firm Gartner too states in a recent report that algorithms are set to transform recruitment processes, in turn helping hiring managers hire the best staff.

Lets take an example, Google used an algorithm to staff up quickly, employing an elaborate survey to zone in on candidates who will fit into the company’s culture. One study of algorithmic hiring found that a simple equation was significantly better than humans at identifying high-performing employees. The result held across different industries and levels of employment, and the researchers attributed the result to humans paying too much attention to inconsequential details and using information about candidates inconsistently.

Gartner’s report titled, “Algorithms Will Transform Talent Acquisition” by Gartner, mentions EdGE Networks, a next-gen HR technology solutions provider, among a handful of algorithm-based solution vendors that could be evaluated by talent acquisition leaders.

According to the Gartner, algorithms evaluating candidates’ suitability for particular roles will replace both manual processing of CVs (resumes) by recruiters and automated CV ranking based on word matching.

Arjun Pratap, founder and CEO of EdGE Networks says, “HR leaders are well aware that excellence in finding, recruiting and retaining great talent is a cornerstone of their organization’s continued success. The algorithm-based solution from EdGE Networks dramatically transforms and optimizes their talent acquisition and workforce optimization processes. We are glad to be named in an influential Gartner report on the subject and we believe it is a great validation of our work.”

The report also recommends that HR, HR IT and talent acquisition leaders should invest now in innovative algorithmic models to support hiring activities, and test these models’ performance over time in context, preferably with a relevant control group. Further, they may keep investing in assessment solutions, while also piloting new predictive models, in order to compare the hiring success rates of either approach.

It states that the ability to use a wide variety of data sources in complex ways by employing machine-learning and data science techniques is resulting in a growing number of unique software solutions for talent acquisition leaders to evaluate.

While a number of HR managers have been dead against this system, stating that machines are eating into their jobs, an article published on  May 2014 issue of Harvard Business Review recommends that by using a purely algorithmic system, HR managers can make better decisions based on a large number of data points, to narrow the field before calling on human judgment to pick from just a few finalists—say, only three. Also, several managers independently weigh in on the final decision, and average their judgments.

At the same time, when an algorithm picks the candidate, they stay longer, a new study from the National Bureau of Economic Research finds. Hiring algorithms have started to gain popularity as a way to reduce hiring and turnover costs, as well as finding employees who fit better within companies, it said.