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A Shift To Algorithm-Driven Decision Making In Recruiting

By Arvind Srivastava, CHRO and Group Head HR, Bansal Group of Companies

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Arvind Srivastava, CHRO and Group Head HR, Bansal Group of Companies

Two years Back, I was part of a board interviewing for a senior position for Modern Retail Business. We were well prepared and, as usual, wanted to know about the candidate Family background, Qualification, Experience and more. Suddenly, one of the board member started questioning the candidate: “Mr. X, how is your son taking the new challenges in Robotics? and “ I hope you are now aligned with his decision choosing Robotics over Met-allurgy”.I was shocked. How did he know all this? There was nothing in his CV related to this and I knew very well that he was meeting Mr. X for the first time.

This was Algorithm Driven Decision shift i.e. Collecting data and processing it to use it as a tool for decision making. It’s a shift from “Tell me something about you” to “I know each and everything about you let me analyse this”.

Human Resource management is all about making decisions and when it comes to Recruitment, decision making is a complicated one where the ability of the candidate is weighed up against the suitability of the candidate. The other reason to choose recruitment may be its efficiency being the low hanging fruit. Recruiting is a high-touch activity that involves stakeholders across the organization.

Now Business needs are changing along with the change in global economy. In today’s world where Boomers and Gen X talent are raised, traditional Hiring approach no longer exists to a large extent. Sourcing, recruiting and hiring approaches for new generations and classifications of talent must also evolve. That requires striking an intricate equilibrium between technology and socialization. As the battle for exceptional talent intensifies, leading staffing professionals are deploying bolder and more creative candidate sourcing strategies. New and emerging technologies, particularly those versed in data analytics, will play prominent roles in talent acquisition this year. AIl startups are significantly reducing the operational burden by automating low level tasks and providing better information for decision makers.

“Recent studies show that the retention of employees who had been hired based on the recommendations of an automated job test is substantially better versus those selected by a human interviewer”

Recent studies show that the retention of employees who had been hired based on the recommendations of an automated job test is substantially better versus those selected by a human interviewer. The automated job test includes questions related to technical skills, personality, cognitive abilities and perceived fit with the company’s business culture. Algorithm driven decision making is not limited to the Recruitment only, the details gathered are being used to engage talent, interacting with them, capturing and maintaining their interest, developing their personal goal towards organizational goals and making career advancement strategies.

But as everything comes with cost, Algorithm-based HR decision-making tools are efficient and objective but downplaying with their potential biases. The challenge identified is that it may shift the delicate balance between employees’ personal integrity and compliance more in the direction of compliance. At the same time, it’s imperative that if you are going to use an algorithm to hire, you need to get it right. This takes time. You need to know what you want, and you need to have the right intelligence to input into the algorithm to ensure it is in alignment with your hiring practices. The critical data literacy, ethical awareness, the use of participatory design methods can help overcome these challenges.

To summarize this, when it comes to sourcing, companies still struggle to get their message through and target the right candidates at the right time, and using algorithm-driven decision making and the technology and recruitment software avail-able, you will be able to make decisions that are based on true evaluation of all the factors. Yet the most successful efforts will be those that include humanity in their algorithms.

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